Publications

Köhne-Fuetterer, Judith; Drenhaus, Heiner; Delogu, Francesca; Demberg, Vera

The online processing of causal and concessive discourse connectives Journal Article

Linguistics, 59, pp. 417-448, 2021.

While there is a substantial amount of evidence for language processing being a highly incremental and predictive process, we still know relatively little about how top-down discourse based expectations are combined with bottom-up information such as discourse connectives. The present article reports on three experiments investigating this question using different methodologies (visual world paradigm and ERPs) in two languages (German and English). We find support for highly incremental processing of causal and concessive discourse connectives, causing anticipation of upcoming material. Our visual world study shows that anticipatory looks depend on the discourse connective; furthermore, the German ERP study revealed an N400 effect on a gender-marked adjective preceding the target noun, when the target noun was inconsistent with the expectations elicited by the combination of context and discourse connective. Moreover, our experiments reveal that the facilitation of downstream material based on earlier connectives comes at the cost of reversing original expectations, as evidenced by a P600 effect on the concessive relative to the causal connective.

@article{koehne2021online,
title = {The online processing of causal and concessive discourse connectives},
author = {Judith K{\"o}hne-Fuetterer and Heiner Drenhaus and Francesca Delogu and Vera Demberg},
url = {https://doi.org/10.1515/ling-2021-0011},
doi = {https://doi.org/doi:10.1515/ling-2021-0011},
year = {2021},
date = {2021-03-04},
journal = {Linguistics},
pages = {417-448},
volume = {59},
number = {2},
abstract = {While there is a substantial amount of evidence for language processing being a highly incremental and predictive process, we still know relatively little about how top-down discourse based expectations are combined with bottom-up information such as discourse connectives. The present article reports on three experiments investigating this question using different methodologies (visual world paradigm and ERPs) in two languages (German and English). We find support for highly incremental processing of causal and concessive discourse connectives, causing anticipation of upcoming material. Our visual world study shows that anticipatory looks depend on the discourse connective; furthermore, the German ERP study revealed an N400 effect on a gender-marked adjective preceding the target noun, when the target noun was inconsistent with the expectations elicited by the combination of context and discourse connective. Moreover, our experiments reveal that the facilitation of downstream material based on earlier connectives comes at the cost of reversing original expectations, as evidenced by a P600 effect on the concessive relative to the causal connective.},
pubstate = {published},
type = {article}
}

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Projects:   A1 B2 B3

Lemke, Tyll Robin; Schäfer, Lisa; Reich, Ingo

Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments Journal Article

PLOS ONE, 16, pp. e0246255, 2021.

We describe a novel approach to estimating the predictability of utterances given extralinguistic context in psycholinguistic research. Predictability effects on language production and comprehension are widely attested, but so far predictability has mostly been manipulated through local linguistic context, which is captured with n-gram language models. However, this method does not allow to investigate predictability effects driven by extralinguistic context. Modeling effects of extralinguistic context is particularly relevant to discourse-initial expressions, which can be predictable even if they lack linguistic context at all. We propose to use script knowledge as an approximation to extralinguistic context. Since the application of script knowledge involves the generation of prediction about upcoming events, we expect that scrips can be used to manipulate the likelihood of linguistic expressions referring to these events. Previous research has shown that script-based discourse expectations modulate the likelihood of linguistic expressions, but script knowledge has often been operationalized with stimuli which were based on researchers’ intuitions and/or expensive production and norming studies. We propose to quantify the likelihood of an utterance based on the probability of the event to which it refers. This probability is calculated with event language models trained on a script knowledge corpus and modulated with probabilistic event chains extracted from the corpus. We use the DeScript corpus of script knowledge to obtain empirically founded estimates of the likelihood of an event to occur in context without having to resort to expensive pre-tests of the stimuli. We exemplify our method at a case study on the usage of nonsentential expressions (fragments), which shows that utterances that are predictable given script-based extralinguistic context are more likely to be reduced.

@article{Lemke2021,
title = {Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments},
author = {Tyll Robin Lemke and Lisa Sch{\"a}fer and Ingo Reich},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246255},
doi = {https://doi.org/10.1371/journal.pone.0246255},
year = {2021},
date = {2021-02-11},
journal = {PLOS ONE},
pages = {e0246255},
volume = {16},
number = {2},
abstract = {We describe a novel approach to estimating the predictability of utterances given extralinguistic context in psycholinguistic research. Predictability effects on language production and comprehension are widely attested, but so far predictability has mostly been manipulated through local linguistic context, which is captured with n-gram language models. However, this method does not allow to investigate predictability effects driven by extralinguistic context. Modeling effects of extralinguistic context is particularly relevant to discourse-initial expressions, which can be predictable even if they lack linguistic context at all. We propose to use script knowledge as an approximation to extralinguistic context. Since the application of script knowledge involves the generation of prediction about upcoming events, we expect that scrips can be used to manipulate the likelihood of linguistic expressions referring to these events. Previous research has shown that script-based discourse expectations modulate the likelihood of linguistic expressions, but script knowledge has often been operationalized with stimuli which were based on researchers’ intuitions and/or expensive production and norming studies. We propose to quantify the likelihood of an utterance based on the probability of the event to which it refers. This probability is calculated with event language models trained on a script knowledge corpus and modulated with probabilistic event chains extracted from the corpus. We use the DeScript corpus of script knowledge to obtain empirically founded estimates of the likelihood of an event to occur in context without having to resort to expensive pre-tests of the stimuli. We exemplify our method at a case study on the usage of nonsentential expressions (fragments), which shows that utterances that are predictable given script-based extralinguistic context are more likely to be reduced.},
pubstate = {published},
type = {article}
}

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Project:   B3

Brouwer, Harm; Delogu, Francesca; Venhuizen, Noortje; Crocker, Matthew W.

Neurobehavioral Correlates of Surprisal in Language Comprehension: A Neurocomputational Model Journal Article

Frontiers in Psychology, 2021.

Expectation-based theories of language comprehension, in particular Surprisal Theory, go a long way in accounting for the behavioral correlates of word-by-word processing difficulty, such as reading times. An open question, however, is in which component(s) of the Event-Related brain Potential (ERP) signal Surprisal is reflected, and how these electrophysiological correlates relate to behavioral processing indices. Here, we address this question by instantiating an explicit neurocomputational model of incremental, word-by-word language comprehension that produces estimates of the N400 and the P600 – the two most salient ERP components for language processing – as well as estimates of `comprehension-centric‘ Surprisal for each word in a sentence. We derive model predictions for a recent experimental design that directly investigates `world-knowledge‘-induced Surprisal. By relating these predictions to both empirical electrophysiological and behavioral results, we establish a close link between Surprisal, as indexed by reading times, and the P600 component of the ERP signal. The resultant model thus offers an integrated neurobehavioral account of processing difficulty in language comprehension.

@article{Brouwer2021,
title = {Neurobehavioral Correlates of Surprisal in Language Comprehension: A Neurocomputational Model},
author = {Harm Brouwer and Francesca Delogu and Noortje Venhuizen and Matthew W. Crocker},
url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2021.615538/full},
doi = {https://doi.org/10.3389/fpsyg.2021.615538},
year = {2021},
date = {2021-02-11},
journal = {Frontiers in Psychology},
abstract = {Expectation-based theories of language comprehension, in particular Surprisal Theory, go a long way in accounting for the behavioral correlates of word-by-word processing difficulty, such as reading times. An open question, however, is in which component(s) of the Event-Related brain Potential (ERP) signal Surprisal is reflected, and how these electrophysiological correlates relate to behavioral processing indices. Here, we address this question by instantiating an explicit neurocomputational model of incremental, word-by-word language comprehension that produces estimates of the N400 and the P600 - the two most salient ERP components for language processing - as well as estimates of `comprehension-centric' Surprisal for each word in a sentence. We derive model predictions for a recent experimental design that directly investigates `world-knowledge'-induced Surprisal. By relating these predictions to both empirical electrophysiological and behavioral results, we establish a close link between Surprisal, as indexed by reading times, and the P600 component of the ERP signal. The resultant model thus offers an integrated neurobehavioral account of processing difficulty in language comprehension.},
pubstate = {published},
type = {article}
}

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Project:   A1

Teich, Elke; Fankhauser, Peter; Degaetano-Ortlieb, Stefania; Bizzoni, Yuri

Less is More/More Diverse: On The Communicative Utility of Linguistic Conventionalization Journal Article

Benîtez-Burraco, Antonio (Ed.): Frontiers in Communication, section Language Sciences, 2021.

We present empirical evidence of the communicative utility of CONVENTIONALIZATION, i.e., convergence in linguistic usage over time, and DIVERSIFICATION, i.e., linguistic items acquiring different, more specific usages/meanings. From a diachronic perspective, conventionalization plays a crucial role in language change as a condition for innovation and grammaticalization (Bybee, 2010; Schmid, 2015) and diversification is a cornerstone in the formation of sublanguages/registers, i.e., functional linguistic varieties (Halliday, 1988; Harris, 1991). While it is widely acknowledged that change in language use is primarily socio-culturally determined pushing towards greater linguistic expressivity, we here highlight the limiting function of communicative factors on diachronic linguistic variation showing that conventionalization and diversification are associated with a reduction of linguistic variability. To be able to observe effects of linguistic variability reduction, we first need a well-defined notion of choice in context. Linguistically, this implies the paradigmatic axis of linguistic organization, i.e., the sets of linguistic options available in a given or similar syntagmatic contexts. Here, we draw on word embeddings, weakly neural distributional language models that have recently been employed to model lexicalsemantic change and allow us to approximate the notion of paradigm by neighbourhood in vector space. Second, we need to capture changes in paradigmatic variability, i.e. reduction/expansion of linguistic options in a given context. As a formal index of paradigmatic variability we use entropy, which measures the contribution of linguistic units (e.g., words) in predicting linguistic choice in bits of information. Using entropy provides us with a link to a communicative interpretation, as it is a well-established measure of communicative efficiency with implications for cognitive processing (Linzen and Jaeger, 2016; Venhuizen et al., 2019); also, entropy is negatively correlated with distance in (word embedding) spaces which in turn shows cognitive reflexes in certain language processing tasks (Mitchel et al., 2008; Auguste et al., 2017). In terms of domain we focus on science, looking at the diachronic development of scientific English from the 17th century to modern time. This provides us with a fairly constrained yet dynamic domain of discourse that has witnessed a powerful systematization throughout the centuries and developed specific linguistic conventions geared towards efficient communication. Overall, our study confirms the assumed trends of conventionalization and diversification shown by diachronically decreasing entropy, interspersed with local, temporary entropy highs pointing to phases of linguistic expansion pertaining primarily to introduction of new technical terminology.

@article{Teich2021,
title = {Less is More/More Diverse: On The Communicative Utility of Linguistic Conventionalization},
author = {Elke Teich and Peter Fankhauser and Stefania Degaetano-Ortlieb and Yuri Bizzoni},
editor = {Antonio Benîtez-Burraco},
url = {https://www.frontiersin.org/articles/10.3389/fcomm.2020.620275/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Communication&id=620275},
doi = {https://doi.org/10.3389/fcomm.2020.620275},
year = {2021},
date = {2021-01-26},
journal = {Frontiers in Communication, section Language Sciences},
abstract = {We present empirical evidence of the communicative utility of CONVENTIONALIZATION, i.e., convergence in linguistic usage over time, and DIVERSIFICATION, i.e., linguistic items acquiring different, more specific usages/meanings. From a diachronic perspective, conventionalization plays a crucial role in language change as a condition for innovation and grammaticalization (Bybee, 2010; Schmid, 2015) and diversification is a cornerstone in the formation of sublanguages/registers, i.e., functional linguistic varieties (Halliday, 1988; Harris, 1991). While it is widely acknowledged that change in language use is primarily socio-culturally determined pushing towards greater linguistic expressivity, we here highlight the limiting function of communicative factors on diachronic linguistic variation showing that conventionalization and diversification are associated with a reduction of linguistic variability. To be able to observe effects of linguistic variability reduction, we first need a well-defined notion of choice in context. Linguistically, this implies the paradigmatic axis of linguistic organization, i.e., the sets of linguistic options available in a given or similar syntagmatic contexts. Here, we draw on word embeddings, weakly neural distributional language models that have recently been employed to model lexicalsemantic change and allow us to approximate the notion of paradigm by neighbourhood in vector space. Second, we need to capture changes in paradigmatic variability, i.e. reduction/expansion of linguistic options in a given context. As a formal index of paradigmatic variability we use entropy, which measures the contribution of linguistic units (e.g., words) in predicting linguistic choice in bits of information. Using entropy provides us with a link to a communicative interpretation, as it is a well-established measure of communicative efficiency with implications for cognitive processing (Linzen and Jaeger, 2016; Venhuizen et al., 2019); also, entropy is negatively correlated with distance in (word embedding) spaces which in turn shows cognitive reflexes in certain language processing tasks (Mitchel et al., 2008; Auguste et al., 2017). In terms of domain we focus on science, looking at the diachronic development of scientific English from the 17th century to modern time. This provides us with a fairly constrained yet dynamic domain of discourse that has witnessed a powerful systematization throughout the centuries and developed specific linguistic conventions geared towards efficient communication. Overall, our study confirms the assumed trends of conventionalization and diversification shown by diachronically decreasing entropy, interspersed with local, temporary entropy highs pointing to phases of linguistic expansion pertaining primarily to introduction of new technical terminology.},
pubstate = {published},
type = {article}
}

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Project:   B1

Kudera, Jacek; Tavi, Lauri; Möbius, Bernd; Avgustinova, Tania; Klakow, Dietrich

The effect of surprisal on articulatory gestures in Polish consonant-to-vowel transitions: A pilot EMA study Inproceedings

14. ITG-Konferenz, ITG-Fachbericht 298: Speech Communication, pp. 179-183, Kiel, Germany, 2021, ISBN 978-3-8007-5627-8.

This study is concerned with the relation between the information-theoretic notion of surprisal and articulatory gesture in Polish consonant-to-vowel transitions. It addresses the question of the influence of diphone predictability on spectral trajectories and articulatory gestures by relating the effect of surprisal with motor fluency. The study combines the computation of locus equations (LE) with kinematic data obtained from electromagnetic articulograph (EMA). The kinematic and acoustic data showed that a small coarticulation effect was present in the highand low-surprisal clusters. Regardless of some small discrepancies across the measures, a high degree of overlap of adjacent segments is reported for the mid-surprisal group in both domains. Two explanations of the observed effect are proposed. The first refers to low-surprisal coarticulation resistance and suggests the need to disambiguate predictable sequences. The second, observed in high surprisal clusters, refers to the prominence given to emphasize the unexpected concatenation.

@inproceedings{Kudera/etal:2021c,
title = {The effect of surprisal on articulatory gestures in Polish consonant-to-vowel transitions: A pilot EMA study},
author = {Jacek Kudera and Lauri Tavi and Bernd M{\"o}bius and Tania Avgustinova and Dietrich Klakow},
url = {https://ieeexplore.ieee.org/document/9657527},
year = {2021},
date = {2021},
booktitle = {14. ITG-Konferenz, ITG-Fachbericht 298: Speech Communication},
isbn = {978-3-8007-5627-8},
pages = {179-183},
address = {Kiel, Germany},
abstract = {This study is concerned with the relation between the information-theoretic notion of surprisal and articulatory gesture in Polish consonant-to-vowel transitions. It addresses the question of the influence of diphone predictability on spectral trajectories and articulatory gestures by relating the effect of surprisal with motor fluency. The study combines the computation of locus equations (LE) with kinematic data obtained from electromagnetic articulograph (EMA). The kinematic and acoustic data showed that a small coarticulation effect was present in the highand low-surprisal clusters. Regardless of some small discrepancies across the measures, a high degree of overlap of adjacent segments is reported for the mid-surprisal group in both domains. Two explanations of the observed effect are proposed. The first refers to low-surprisal coarticulation resistance and suggests the need to disambiguate predictable sequences. The second, observed in high surprisal clusters, refers to the prominence given to emphasize the unexpected concatenation.},
pubstate = {published},
type = {inproceedings}
}

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Project:   C4

Kudera, Jacek; Georgis, Philip; Möbius, Bernd; Avgustinova, Tania; Klakow, Dietrich

Phonetic Distance and Surprisal in Multilingual Priming: Evidence from Slavic Inproceedings

Proc. Interspeech, pp. 3944-3948, 2021.

This study reveals the relation between surprisal, phonetic distance, and latency based on a multilingual, short-term priming framework. Four Slavic languages (Bulgarian, Czech, Polish, and Russian) are investigated across two priming conditions: associative and phonetic priming, involving true cognates and near-homophones, respectively. This research is grounded in the methodology of information theory and proposes new methods for quantifying differences between meaningful lexical primes and targets for closely related languages. It also outlines the influence of phonetic distance between cognate and noncognate pairs of primes and targets on response times in a cross-lingual lexical decision task. The experimental results show that phonetic distance moderates response times only in Polish and Czech, whereas the surprisal-based correspondence effect is an accurate predictor of latency for all tested languages. The information-theoretic approach of quantifying feature-based alternations between Slavic cognates and near-homophones appears to be a valid method for latency moderation in the auditory modality. The outcomes of this study suggest that the surprisal-based (un)expectedness of spoken stimuli is an accurate predictor of human performance in multilingual lexical decision tasks.

@inproceedings{kudera21_interspeech,
title = {Phonetic Distance and Surprisal in Multilingual Priming: Evidence from Slavic},
author = {Jacek Kudera and Philip Georgis and Bernd M{\"o}bius and Tania Avgustinova and Dietrich Klakow},
url = {https://www.isca-speech.org/archive/interspeech_2021/kudera21_interspeech.html},
doi = {https://doi.org/10.21437/Interspeech.2021-1003},
year = {2021},
date = {2021},
booktitle = {Proc. Interspeech},
pages = {3944-3948},
abstract = {This study reveals the relation between surprisal, phonetic distance, and latency based on a multilingual, short-term priming framework. Four Slavic languages (Bulgarian, Czech, Polish, and Russian) are investigated across two priming conditions: associative and phonetic priming, involving true cognates and near-homophones, respectively. This research is grounded in the methodology of information theory and proposes new methods for quantifying differences between meaningful lexical primes and targets for closely related languages. It also outlines the influence of phonetic distance between cognate and noncognate pairs of primes and targets on response times in a cross-lingual lexical decision task. The experimental results show that phonetic distance moderates response times only in Polish and Czech, whereas the surprisal-based correspondence effect is an accurate predictor of latency for all tested languages. The information-theoretic approach of quantifying feature-based alternations between Slavic cognates and near-homophones appears to be a valid method for latency moderation in the auditory modality. The outcomes of this study suggest that the surprisal-based (un)expectedness of spoken stimuli is an accurate predictor of human performance in multilingual lexical decision tasks.},
pubstate = {published},
type = {inproceedings}
}

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Project:   C4

Abdullah, Badr M.; Zaitova, Iuliia; Avgustinova, Tania; Möbius, Bernd; Klakow, Dietrich

How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings Inproceedings

Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, Association for Computational Linguistics, pp. 407-419, 2021.

How do neural networks “perceive” speech sounds from unknown languages? Does the typological similarity between the model’s training language (L1) and an unknown language (L2) have an impact on the model representations of L2 speech signals? To answer these questions, we present a novel experimental design based on representational similarity analysis (RSA) to analyze acoustic word embeddings (AWEs)—vector representations of variable-duration spoken-word segments. First, we train monolingual AWE models on seven Indo-European languages with various degrees of typological similarity. We then employ RSA to quantify the cross-lingual similarity by simulating native and non-native spoken-word processing using AWEs. Our experiments show that typological similarity indeed affects the representational similarity of the models in our study. We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.

@inproceedings{abdullah-etal-2021-familiar,
title = {How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings},
author = {Badr M. Abdullah and Iuliia Zaitova and Tania Avgustinova and Bernd M{\"o}bius and Dietrich Klakow},
url = {https://aclanthology.org/2021.blackboxnlp-1.32/},
doi = {https://doi.org/10.18653/v1/2021.blackboxnlp-1.32},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP},
pages = {407-419},
publisher = {Association for Computational Linguistics},
abstract = {How do neural networks “perceive” speech sounds from unknown languages? Does the typological similarity between the model’s training language (L1) and an unknown language (L2) have an impact on the model representations of L2 speech signals? To answer these questions, we present a novel experimental design based on representational similarity analysis (RSA) to analyze acoustic word embeddings (AWEs)—vector representations of variable-duration spoken-word segments. First, we train monolingual AWE models on seven Indo-European languages with various degrees of typological similarity. We then employ RSA to quantify the cross-lingual similarity by simulating native and non-native spoken-word processing using AWEs. Our experiments show that typological similarity indeed affects the representational similarity of the models in our study. We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.},
pubstate = {published},
type = {inproceedings}
}

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Project:   C4

Zouhar, Vilém; Mosbach, Marius; Biswas, Debanjali; Klakow, Dietrich

Artefact Retrieval: Overview of NLP Models with Knowledge Base Access Inproceedings

Workshop on Commonsense Reasoning and Knowledge Bases, 2021.

Many NLP models gain performance by having access to a knowledge base. A lot of research has been devoted to devising and improving the way the knowledge base is accessed and incorporated into the model, resulting in a number of mechanisms and pipelines. Despite the diversity of proposed mechanisms, there are patterns in the designs of such systems. In this paper, we systematically describe the typology of *artefacts* (items retrieved from a knowledge base), retrieval mechanisms and the way these artefacts are *fused* into the model. This further allows us to uncover combinations of design decisions that had not yet been tried. Most of the focus is given to language models, though we also show how question answering, fact-checking and knowledgable dialogue models fit into this system as well. Having an abstract model which can describe the architecture of specific models also helps with transferring these architectures between multiple NLP tasks.

@inproceedings{zouhar2021artefact,
title = {Artefact Retrieval: Overview of NLP Models with Knowledge Base Access},
author = {Vil{\'e}m Zouhar and Marius Mosbach and Debanjali Biswas and Dietrich Klakow},
url = {https://arxiv.org/abs/2201.09651},
year = {2021},
date = {2021},
booktitle = {Workshop on Commonsense Reasoning and Knowledge Bases},
abstract = {Many NLP models gain performance by having access to a knowledge base. A lot of research has been devoted to devising and improving the way the knowledge base is accessed and incorporated into the model, resulting in a number of mechanisms and pipelines. Despite the diversity of proposed mechanisms, there are patterns in the designs of such systems. In this paper, we systematically describe the typology of *artefacts* (items retrieved from a knowledge base), retrieval mechanisms and the way these artefacts are *fused* into the model. This further allows us to uncover combinations of design decisions that had not yet been tried. Most of the focus is given to language models, though we also show how question answering, fact-checking and knowledgable dialogue models fit into this system as well. Having an abstract model which can describe the architecture of specific models also helps with transferring these architectures between multiple NLP tasks.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B4

Hoek, Jet; Scholman, Merel; Sanders, Ted J. M.

Is there less agreement when the discourse is underspecified? Inproceedings

Proceedings of the Integrating Perspectives on Discourse Annotation (DiscAnn) Workshop, University of Tübingen, Germany, 2021.

When annotating coherence relations, interannotator agreement tends to be lower on implicit relations than on relations that are explicitly marked by means of a connective or a cue phrase. This paper explores one possible explanation for this: the additional inferencing involved in interpreting implicit relations compared to explicit relations. If this is the main source of disagreements, agreement should be highly related to the specificity of the connective. Using the CCR framework, we annotated relations from TED talks that were marked by a very specific marker, marked by a highly ambiguous connective, or not marked by means of a connective at all. We indeed reached higher inter-annotator agreement on explicit than on implicit relations. However, agreement on underspecified relations was not necessarily in between, which is what would be expected if agreement on implicit relations mainly suffers because annotators have less specific instructions for inferring the relation.

@inproceedings{hoek-etal-2021-discann,
title = {Is there less agreement when the discourse is underspecified?},
author = {Jet Hoek and Merel Scholman and Ted J. M. Sanders},
url = {https://aclanthology.org/2021.discann-1.1/},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Integrating Perspectives on Discourse Annotation (DiscAnn) Workshop},
address = {University of T{\"u}bingen, Germany},
abstract = {When annotating coherence relations, interannotator agreement tends to be lower on implicit relations than on relations that are explicitly marked by means of a connective or a cue phrase. This paper explores one possible explanation for this: the additional inferencing involved in interpreting implicit relations compared to explicit relations. If this is the main source of disagreements, agreement should be highly related to the specificity of the connective. Using the CCR framework, we annotated relations from TED talks that were marked by a very specific marker, marked by a highly ambiguous connective, or not marked by means of a connective at all. We indeed reached higher inter-annotator agreement on explicit than on implicit relations. However, agreement on underspecified relations was not necessarily in between, which is what would be expected if agreement on implicit relations mainly suffers because annotators have less specific instructions for inferring the relation.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Yung, Frances Pik Yu; Scholman, Merel; Demberg, Vera

A practical perspective on connective generation Inproceedings

Proceedings of the Second Workshop on Computational Approaches to Discourse (CODI), Association for Computational Linguistics, pp. 72-83, Punta Cana, Dominican Republic and Online, 2021.

In data-driven natural language generation, we typically know what relation should be expressed and need to select a connective to lexicalize it. In the current contribution, we analyse whether a sophisticated connective generation module is necessary to select a connective, or whether this can be solved with simple methods (such as random choice between connectives that are known to express a given relation, or usage of a generic language model). Comparing these methods to the distributions of connective choices from a human connective insertion task, we find mixed results: for some relations, it is acceptable to lexicalize them using any of the connectives that mark this relation. However, for other relations (temporals, concessives) either a more detailed relation distinction needs to be introduced, or a more sophisticated connective choice module would be necessary.

@inproceedings{yung-etal-2021-practical,
title = {A practical perspective on connective generation},
author = {Frances Pik Yu Yung and Merel Scholman and Vera Demberg},
url = {https://aclanthology.org/2021.codi-main.7},
doi = {https://doi.org/10.18653/v1/2021.codi-main.7},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Second Workshop on Computational Approaches to Discourse (CODI)},
pages = {72-83},
publisher = {Association for Computational Linguistics},
address = {Punta Cana, Dominican Republic and Online},
abstract = {In data-driven natural language generation, we typically know what relation should be expressed and need to select a connective to lexicalize it. In the current contribution, we analyse whether a sophisticated connective generation module is necessary to select a connective, or whether this can be solved with simple methods (such as random choice between connectives that are known to express a given relation, or usage of a generic language model). Comparing these methods to the distributions of connective choices from a human connective insertion task, we find mixed results: for some relations, it is acceptable to lexicalize them using any of the connectives that mark this relation. However, for other relations (temporals, concessives) either a more detailed relation distinction needs to be introduced, or a more sophisticated connective choice module would be necessary.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Scholman, Merel; Dong, Tianai; Yung, Frances Pik Yu; Demberg, Vera

Comparison of methods for explicit discourse connective identification across various domains Inproceedings

Proceedings of the Second Workshop on Computational Approaches to Discourse (CODI), Association for Computational Linguistics, pp. 95-106, Punta Cana, Dominican Republic and Online, 2021.

Existing parse methods use varying approaches to identify explicit discourse connectives, but their performance has not been consistently evaluated in comparison to each other, nor have they been evaluated consistently on text other than newspaper articles. We here assess the performance on explicit connective identification of three parse methods (PDTB e2e, Lin et al., 2014; the winner of CONLL2015, Wang et al., 2015; and DisSent, Nie et al., 2019), along with a simple heuristic. We also examine how well these systems generalize to different datasets, namely written newspaper text (PDTB), written scientific text (BioDRB), prepared spoken text (TED-MDB) and spontaneous spoken text (Disco-SPICE). The results show that the e2e parser outperforms the other parse methods in all datasets. However, performance drops significantly from the PDTB to all other datasets. We provide a more fine-grained analysis of domain differences and connectives that prove difficult to parse, in order to highlight the areas where gains can be made.

@inproceedings{scholman-etal-2021-comparison,
title = {Comparison of methods for explicit discourse connective identification across various domains},
author = {Merel Scholman and Tianai Dong and Frances Pik Yu Yung and Vera Demberg},
url = {https://aclanthology.org/2021.codi-main.9},
doi = {https://doi.org/10.18653/v1/2021.codi-main.9},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Second Workshop on Computational Approaches to Discourse (CODI)},
pages = {95-106},
publisher = {Association for Computational Linguistics},
address = {Punta Cana, Dominican Republic and Online},
abstract = {Existing parse methods use varying approaches to identify explicit discourse connectives, but their performance has not been consistently evaluated in comparison to each other, nor have they been evaluated consistently on text other than newspaper articles. We here assess the performance on explicit connective identification of three parse methods (PDTB e2e, Lin et al., 2014; the winner of CONLL2015, Wang et al., 2015; and DisSent, Nie et al., 2019), along with a simple heuristic. We also examine how well these systems generalize to different datasets, namely written newspaper text (PDTB), written scientific text (BioDRB), prepared spoken text (TED-MDB) and spontaneous spoken text (Disco-SPICE). The results show that the e2e parser outperforms the other parse methods in all datasets. However, performance drops significantly from the PDTB to all other datasets. We provide a more fine-grained analysis of domain differences and connectives that prove difficult to parse, in order to highlight the areas where gains can be made.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Lemke, Tyll Robin

Satzäquivalente — Syntax oder Pragmatik? Incollection

Külpmann, Robert; Finkbeiner, Rita;  (Ed.): Neues zur Selbstständigkeit von Sätzen, Linguistische Berichte, Sonderheft, Buske, pp. 81-104, Hamburg, 2021, ISBN 978-3-96769-170-2 .

„Satzäquivalente“ scheinen einen Widerspruch zwischen Syntax und Pragmatik darzustellen, da sie trotz nichtsententialer Form die selben Funktionen wie Sätze erfüllen. Wir stellen zwei Experimente vor, die Vorhersagen zweier theoretischer Perspektiven auf diese Ausdrücke untersuchen. Einerseits generieren elliptische Ansätze (Morgan, 1973; Merchant, 2004; Reich, 2007) Satzäquivalente mittels Ellipse aus vollständigen Sätzen, andererseits schlagen nichtsententiale Ansätze (Barton & Progovac, 2005; Stainton, 2006) vor, dass die Syntax subsententiale Ausdrücke generieren kann.

@incollection{Lemke2021a,
title = {Satz{\"a}quivalente — Syntax oder Pragmatik?},
author = {Tyll Robin Lemke},
editor = {Robert K{\"u}lpmann and Rita Finkbeiner},
url = {https://buske.de/zeitschriften-bei-sonderhefte/linguistische-berichte-sonderhefte/neues-zur-selbststandigkeit-von-satzen-16620.html},
doi = {https://doi.org/10.46771/978-3-96769-170-2},
year = {2021},
date = {2021},
booktitle = {Neues zur Selbstst{\"a}ndigkeit von S{\"a}tzen},
isbn = {978-3-96769-170-2},
pages = {81-104},
publisher = {Buske},
address = {Hamburg},
abstract = {"Satz{\"a}quivalente" scheinen einen Widerspruch zwischen Syntax und Pragmatik darzustellen, da sie trotz nichtsententialer Form die selben Funktionen wie S{\"a}tze erf{\"u}llen. Wir stellen zwei Experimente vor, die Vorhersagen zweier theoretischer Perspektiven auf diese Ausdr{\"u}cke untersuchen. Einerseits generieren elliptische Ans{\"a}tze (Morgan, 1973; Merchant, 2004; Reich, 2007) Satz{\"a}quivalente mittels Ellipse aus vollst{\"a}ndigen S{\"a}tzen, andererseits schlagen nichtsententiale Ans{\"a}tze (Barton & Progovac, 2005; Stainton, 2006) vor, dass die Syntax subsententiale Ausdr{\"u}cke generieren kann.},
pubstate = {published},
type = {incollection}
}

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Project:   B3

Lemke, Tyll Robin

Experimental investigations on the syntax and usage of fragments Miscellaneous

Experimental investigations on the syntax and usage of fragments, Open Germanic Linguistics, Language Science Press, Berlin, 2021.

This book investigates the syntax and usage of fragments (Morgan 1973), apparently subsentential utterances like „A coffee, please!“ which fulfill the same communicative function as the corresponding full sentence „I’d like to have a coffee, please!“. Even though such utterances are frequently used, they challenge the central role that has been attributed to the notion of sentence in linguistic theory, particularly from a semantic perspective.

The first part of the book is dedicated to the syntactic analysis of fragments, which is investigated with experimental methods. Currently there are several competing theoretical analyses of fragments, which rely almost only on introspective data. The experiments presented in this book constitute a first systematic evaluation of some of their crucial predictions and, taken together, support an in situ ellipsis account of fragments, as has been suggested by Reich (2007).

The second part of the book addresses the questions of why fragments are used at all, and under which circumstances they are preferred over complete sentences. Syntactic accounts impose licensing conditions on fragments, but they do not explain, why fragments are sometimes (dis)preferred provided that their usage is licensed. This book proposes an information-theoretic account of fragments, which predicts that the usage of fragments in constrained by a general tendency to distribute processing effort uniformly across the utterance. With respect to fragments, this leads to two predictions, which are empirically confirmed: Speakers tend towards omitting predictable words and they insert additional redundancy before unpredictable words.

@miscellaneous{Lemke2021,
title = {Experimental investigations on the syntax and usage of fragments},
author = {Tyll Robin Lemke},
url = {https://langsci-press.org/catalog/book/321},
doi = {https://doi.org/10.5281/zenodo.5596236},
year = {2021},
date = {2021},
booktitle = {Experimental investigations on the syntax and usage of fragments},
publisher = {Language Science Press},
address = {Berlin},
abstract = {This book investigates the syntax and usage of fragments (Morgan 1973), apparently subsentential utterances like "A coffee, please!" which fulfill the same communicative function as the corresponding full sentence "I'd like to have a coffee, please!". Even though such utterances are frequently used, they challenge the central role that has been attributed to the notion of sentence in linguistic theory, particularly from a semantic perspective. The first part of the book is dedicated to the syntactic analysis of fragments, which is investigated with experimental methods. Currently there are several competing theoretical analyses of fragments, which rely almost only on introspective data. The experiments presented in this book constitute a first systematic evaluation of some of their crucial predictions and, taken together, support an in situ ellipsis account of fragments, as has been suggested by Reich (2007). The second part of the book addresses the questions of why fragments are used at all, and under which circumstances they are preferred over complete sentences. Syntactic accounts impose licensing conditions on fragments, but they do not explain, why fragments are sometimes (dis)preferred provided that their usage is licensed. This book proposes an information-theoretic account of fragments, which predicts that the usage of fragments in constrained by a general tendency to distribute processing effort uniformly across the utterance. With respect to fragments, this leads to two predictions, which are empirically confirmed: Speakers tend towards omitting predictable words and they insert additional redundancy before unpredictable words.},
pubstate = {published},
type = {miscellaneous}
}

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Project:   B3

Kalimuthu, Marimuthu; Mogadala, Aditya; Mosbach, Marius; Klakow, Dietrich

Fusion Models for Improved Image Captioning Inproceedings

Pattern Recognition. ICPR International Workshops and Challenges, pp. 381-395, Cham, 2020.

Visual captioning aims to generate textual descriptions given images or videos. Traditionally, image captioning models are trained on human annotated datasets such as Flickr30k and MS-COCO, which are limited in size and diversity. This limitation hinders the generalization capabilities of these models while also rendering them liable to making mistakes. Language models can, however, be trained on vast amounts of freely available unlabelled data and have recently emerged as successful language encoders and coherent text generators. Meanwhile, several unimodal and multimodal fusion techniques have been proven to work well for natural language generation and automatic speech recognition. Building on these recent developments, and with the aim of improving the quality of generated captions, the contribution of our work in this paper is two-fold: First, we propose a generic multimodal model fusion framework for caption generation as well as emendation where we utilize different fusion strategies to integrate a pretrained Auxiliary Language Model (AuxLM) within the traditional encoder-decoder visual captioning frameworks. Next, we employ the same fusion strategies to integrate a pretrained Masked Language Model (MLM), namely BERT, with a visual captioning model, viz. Show, Attend, and Tell, for emending both syntactic and semantic errors in captions. Our caption emendation experiments on three benchmark image captioning datasets, viz. Flickr8k, Flickr30k, and MSCOCO, show improvements over the baseline, indicating the usefulness of our proposed multimodal fusion strategies. Further, we perform a preliminary qualitative analysis on the emended captions and identify error categories based on the type of corrections.

@inproceedings{Kalimuthu2021fusion,
title = {Fusion Models for Improved Image Captioning},
author = {Marimuthu Kalimuthu and Aditya Mogadala and Marius Mosbach and Dietrich Klakow},
url = {https://arxiv.org/abs/2010.15251},
doi = {https://doi.org/10.1007/978-3-030-68780-9_32},
year = {2020},
date = {2020},
booktitle = {Pattern Recognition. ICPR International Workshops and Challenges},
pages = {381-395},
address = {Cham},
abstract = {Visual captioning aims to generate textual descriptions given images or videos. Traditionally, image captioning models are trained on human annotated datasets such as Flickr30k and MS-COCO, which are limited in size and diversity. This limitation hinders the generalization capabilities of these models while also rendering them liable to making mistakes. Language models can, however, be trained on vast amounts of freely available unlabelled data and have recently emerged as successful language encoders and coherent text generators. Meanwhile, several unimodal and multimodal fusion techniques have been proven to work well for natural language generation and automatic speech recognition. Building on these recent developments, and with the aim of improving the quality of generated captions, the contribution of our work in this paper is two-fold: First, we propose a generic multimodal model fusion framework for caption generation as well as emendation where we utilize different fusion strategies to integrate a pretrained Auxiliary Language Model (AuxLM) within the traditional encoder-decoder visual captioning frameworks. Next, we employ the same fusion strategies to integrate a pretrained Masked Language Model (MLM), namely BERT, with a visual captioning model, viz. Show, Attend, and Tell, for emending both syntactic and semantic errors in captions. Our caption emendation experiments on three benchmark image captioning datasets, viz. Flickr8k, Flickr30k, and MSCOCO, show improvements over the baseline, indicating the usefulness of our proposed multimodal fusion strategies. Further, we perform a preliminary qualitative analysis on the emended captions and identify error categories based on the type of corrections.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B4

Mogadala, Aditya; Mosbach, Marius; Klakow, Dietrich

Sparse Graph to Sequence Learning for Vision Conditioned Long Textual Sequence Generation Inproceedings

Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond, Workshop at ICML, 2020.

Generating longer textual sequences when conditioned on the visual information is an interesting problem to explore. The challenge here proliferate over the standard vision conditioned sentence-level generation (e.g., image or video captioning) as it requires to produce a brief and coherent story describing the visual content. In this paper, we mask this Vision-to-Sequence as Graph-to-Sequence learning problem and approach it with the Transformer architecture. To be specific, we introduce Sparse Graph-to-Sequence Transformer (SGST) for encoding the graph and decoding a sequence. The encoder aims to directly encode graph-level semantics, while the decoder is used to generate longer sequences. Experiments conducted with the benchmark image paragraph dataset show that our proposed achieve 13.3% improvement on the CIDEr evaluation measure when comparing to the previous state-of-the-art approach.

@inproceedings{mogadala2020sparse,
title = {Sparse Graph to Sequence Learning for Vision Conditioned Long Textual Sequence Generation},
author = {Aditya Mogadala and Marius Mosbach and Dietrich Klakow},
url = {https://arxiv.org/abs/2007.06077},
year = {2020},
date = {2020},
booktitle = {Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond, Workshop at ICML},
abstract = {Generating longer textual sequences when conditioned on the visual information is an interesting problem to explore. The challenge here proliferate over the standard vision conditioned sentence-level generation (e.g., image or video captioning) as it requires to produce a brief and coherent story describing the visual content. In this paper, we mask this Vision-to-Sequence as Graph-to-Sequence learning problem and approach it with the Transformer architecture. To be specific, we introduce Sparse Graph-to-Sequence Transformer (SGST) for encoding the graph and decoding a sequence. The encoder aims to directly encode graph-level semantics, while the decoder is used to generate longer sequences. Experiments conducted with the benchmark image paragraph dataset show that our proposed achieve 13.3% improvement on the CIDEr evaluation measure when comparing to the previous state-of-the-art approach.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B4

Ferber, Patrick; Hoffmann, Jörg; Helmert, Malte

Neural network heuristics for classical planning: A study of hyperparameter space Inproceedings

24th European Conference on Artificial Intelligence (ECAI’20), 2020.

Neural networks (NN) have been shown to be powerful state-value predictors in several complex games. Can similar successes be achieved in classical planning? Towards a systematic exploration of that question, we contribute a study of hyperparameter space in the most canonical setup: input = state, feed-forward NN, supervised learning, generalization only over initial state. We investigate a broad range of hyperparameters pertaining to NN design and training. We evaluate these techniques through their use as heuristic functions in Fast Downward. The results on IPC benchmarks show that highly competitive heuristics can be learned, yielding substantially smaller search spaces than standard techniques on some domains. But the heuristic functions are costly to evaluate, and the range of domains where useful heuristics are learned is limited. Our study provides the basis for further research improving on current weaknesses.

@inproceedings{Ferber2020network,
title = {Neural network heuristics for classical planning: A study of hyperparameter space},
author = {Patrick Ferber and J{\"o}rg Hoffmann and Malte Helmert},
url = {https://ecai2020.eu/papers/433_paper.pdf},
year = {2020},
date = {2020},
booktitle = {24th European Conference on Artificial Intelligence (ECAI’20)},
abstract = {Neural networks (NN) have been shown to be powerful state-value predictors in several complex games. Can similar successes be achieved in classical planning? Towards a systematic exploration of that question, we contribute a study of hyperparameter space in the most canonical setup: input = state, feed-forward NN, supervised learning, generalization only over initial state. We investigate a broad range of hyperparameters pertaining to NN design and training. We evaluate these techniques through their use as heuristic functions in Fast Downward. The results on IPC benchmarks show that highly competitive heuristics can be learned, yielding substantially smaller search spaces than standard techniques on some domains. But the heuristic functions are costly to evaluate, and the range of domains where useful heuristics are learned is limited. Our study provides the basis for further research improving on current weaknesses.},
pubstate = {published},
type = {inproceedings}
}

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Project:   A7

Mecklinger, Axel; Bader, Regine

From fluency to recognition decisions: A broader view of familiarity-based remembering Journal Article

Neuropsychologia, 146, pp. 107527, 2020.

The goal of this article is to critically examine current claims and assumptions about the FN400, an event-related potential (ERP) component which has been related to familiarity memory though some uncertainty exists regarding the cognitive processes captured by the FN400. It is proposed that familiarity can be multiply determined and that an important distinction has to be made between a recent-exposure, relative familiarity mechanism indexed by the FN400 and an absolute/baseline familiarity mechanism being reflected by a coincidental but topographically distinct ERP effect. We suggest a broader conceptualization of the memory processes reflected by the FN400 and propose an unexpected fluency-attribution account of familiarity according to which familiarity results from a fast assessment of ongoing processing fluency relative to previous events or current expectations. The computations underlying fluency attribution may be closely related to those characterizing the relative familiarity mechanism underlying the FN400. We also argue that concerted activation of the perirhinal cortex (PrC) and the lateral prefrontal cortex (PFC) plays a pivotal role for fluency attributions and the generation of the FN400.

@article{MecklingerBader2020,
title = {From fluency to recognition decisions: A broader view of familiarity-based remembering},
author = {Axel Mecklinger and Regine Bader},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0028393220302001},
doi = {https://doi.org/10.1016/j.neuropsychologia.2020.107527},
year = {2020},
date = {2020},
journal = {Neuropsychologia},
pages = {107527},
volume = {146},
abstract = {The goal of this article is to critically examine current claims and assumptions about the FN400, an event-related potential (ERP) component which has been related to familiarity memory though some uncertainty exists regarding the cognitive processes captured by the FN400. It is proposed that familiarity can be multiply determined and that an important distinction has to be made between a recent-exposure, relative familiarity mechanism indexed by the FN400 and an absolute/baseline familiarity mechanism being reflected by a coincidental but topographically distinct ERP effect. We suggest a broader conceptualization of the memory processes reflected by the FN400 and propose an unexpected fluency-attribution account of familiarity according to which familiarity results from a fast assessment of ongoing processing fluency relative to previous events or current expectations. The computations underlying fluency attribution may be closely related to those characterizing the relative familiarity mechanism underlying the FN400. We also argue that concerted activation of the perirhinal cortex (PrC) and the lateral prefrontal cortex (PFC) plays a pivotal role for fluency attributions and the generation of the FN400.},
pubstate = {published},
type = {article}
}

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Project:   A6

Höltje, Gerrit; Mecklinger, Axel

Feedback timing modulates interactions between reward learning and memory encoding: Evidence from event-related potentials Journal Article

Cognitive, Affective and Behavioral Neuroscience, 20, pp. 250-264, 2020.

Feedback-based learning relies on a procedural learning system driven by reward prediction errors (RPEs). The processing of temporally delayed feedback is supported by brain structures associated with declarative memory processes, but it is still unknown how delayed feedback processing and memory encoding interact. In this study, a subsequent memory paradigm was employed to investigate how the incidental encoding of feedback pictures presented with a short (SD, 500 ms) or long (LD, 6500 ms) delay in a probabilistic learning task affects the event-related potential (ERP) correlate of RPEs (i.e., the feedback-related negativity; FRN). In an ensuing test phase, a surprise recognition memory test for the feedback pictures was conducted. FRN amplitudes measured in the feedback-locked ERPs recorded during the learning phase (FRNpeak) and in the negative minus positive feedback difference wave (FRNdiff) were compared for subsequently remembered and forgotten feedback pictures. Feedback processing as reflected in the FRNpeak was diminished for remembered LD feedback pictures, indicating that delayed feedback processing and memory encoding competed for similar neural processing resources. As evidenced by large FRNdiff amplitudes in the SD condition, the evaluation of shortly delayed feedback strongly relied on the procedural learning system. A complementary model-based single trial analysis was conducted to validate models of the functional significance of the FRN. Consistent with previous studies, feedback-locked N170 and P300 amplitudes were sensitive to feedback delay. In the test phase, memory for LD feedback pictures was better than for SD pictures and accompanied by a late old-new effect, presumably reflecting extended recollective processing.

@article{hoeltje2020feedback,
title = {Feedback timing modulates interactions between reward learning and memory encoding: Evidence from event-related potentials},
author = {Gerrit H{\"o}ltje and Axel Mecklinger},
url = {https://pubmed.ncbi.nlm.nih.gov/31900874/},
doi = {https://doi.org/10.3758/s13415-019-00765-5},
year = {2020},
date = {2020},
journal = {Cognitive, Affective and Behavioral Neuroscience},
pages = {250-264},
volume = {20},
number = {2},
abstract = {Feedback-based learning relies on a procedural learning system driven by reward prediction errors (RPEs). The processing of temporally delayed feedback is supported by brain structures associated with declarative memory processes, but it is still unknown how delayed feedback processing and memory encoding interact. In this study, a subsequent memory paradigm was employed to investigate how the incidental encoding of feedback pictures presented with a short (SD, 500 ms) or long (LD, 6500 ms) delay in a probabilistic learning task affects the event-related potential (ERP) correlate of RPEs (i.e., the feedback-related negativity; FRN). In an ensuing test phase, a surprise recognition memory test for the feedback pictures was conducted. FRN amplitudes measured in the feedback-locked ERPs recorded during the learning phase (FRNpeak) and in the negative minus positive feedback difference wave (FRNdiff) were compared for subsequently remembered and forgotten feedback pictures. Feedback processing as reflected in the FRNpeak was diminished for remembered LD feedback pictures, indicating that delayed feedback processing and memory encoding competed for similar neural processing resources. As evidenced by large FRNdiff amplitudes in the SD condition, the evaluation of shortly delayed feedback strongly relied on the procedural learning system. A complementary model-based single trial analysis was conducted to validate models of the functional significance of the FRN. Consistent with previous studies, feedback-locked N170 and P300 amplitudes were sensitive to feedback delay. In the test phase, memory for LD feedback pictures was better than for SD pictures and accompanied by a late old-new effect, presumably reflecting extended recollective processing.},
pubstate = {published},
type = {article}
}

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Project:   A6

Ortmann, Katrin

Automatic Topological Field Identification in (Historical) German Texts Inproceedings

Proceedings of the 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 10-18, Barcelona, Spain (online), 2020.

For the study of certain linguistic phenomena and their development over time, large amounts of textual data must be enriched with relevant annotations. Since the manual creation of such annotations requires a lot of effort, automating the process with NLP methods would be convenient. But the required amounts of training data are usually not available for non-standard or historical language. The present study investigates whether models trained on modern newspaper text can be used to automatically identify topological fields, i.e. syntactic structures, in different modern and historical German texts. The evaluation shows that, in general, it is possible to transfer a parser model to other registers or time periods with overall F1-scores >92%. However, an error analysis makes clear that additional rules and domain-specific training data would be beneficial if sentence structures differ significantly from the training data, e.g. in the case of Early New High German.

@inproceedings{Ortmann2020b,
title = {Automatic Topological Field Identification in (Historical) German Texts},
author = {Katrin Ortmann},
url = {https://www.aclweb.org/anthology/2020.latechclfl-1.2},
year = {2020},
date = {2020-12-12},
booktitle = {Proceedings of the 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature},
pages = {10-18},
address = {Barcelona, Spain (online)},
abstract = {For the study of certain linguistic phenomena and their development over time, large amounts of textual data must be enriched with relevant annotations. Since the manual creation of such annotations requires a lot of effort, automating the process with NLP methods would be convenient. But the required amounts of training data are usually not available for non-standard or historical language. The present study investigates whether models trained on modern newspaper text can be used to automatically identify topological fields, i.e. syntactic structures, in different modern and historical German texts. The evaluation shows that, in general, it is possible to transfer a parser model to other registers or time periods with overall F1-scores >92%. However, an error analysis makes clear that additional rules and domain-specific training data would be beneficial if sentence structures differ significantly from the training data, e.g. in the case of Early New High German.},
pubstate = {published},
type = {inproceedings}
}

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Project:   C6

Höltje, Gerrit

Interactions between immediate and delayed feedback processing and memory encoding: an investigation using event-related potentials PhD Thesis

Saarland University, Saarbruecken, Germany, 2020.

Feedback-based learning relies on a procedural learning system mediated by dopaminergic reward prediction error (RPE) signals. Recent neuroimaging research indicates that the processing of temporally delayed feedback is supported by the hippocampus, a brain structure associated with declarative memory processes, but it is still unknown how delayed feedback processing and memory encoding interact. In this dissertation project, in a series of three experiments, a subsequent memory paradigm was employed to investigate how the incidental encoding of feedback pictures in a probabilistic learning task affects the event-related potential (ERP) correlate of RPEs in feedback processing, i.e., the feedback-related negativity (FRN), and how this interaction is modulated by feedback timing, valence, and explicit outcome expectations. In Experiment 1, task-unrelated scene pictures were presented together with performance feedback in the learning task. In an ensuing test phase, a surprise recognition memory test for the pictures was conducted. FRN amplitudes measured in the feedback-locked ERPs recorded during the learning phase (FRNpeak) and in the negative minus positive feedback difference wave (FRNdiff) were compared for subsequently remembered and forgotten feedback pictures. Pictures were remembered better when presented together with positive than with negative feedback, and ERP amplitudes in the FRNdiff time window predicted subsequent memory only for positive feedback pictures. Consistent with previous studies, shortly delayed (SD, 500 ms) feedback elicited larger FRNdiff amplitudes than long delayed feedback (LD, 6500 ms), whereas the reverse pattern was found in FRNpeak amplitudes. As evidenced by behavioral estimates and ERP old/new effects, positive feedback enhanced memory by boosting familiarity-based recognition. However, feedback timing did not affect memory, presumably because participants did not need to process the scene pictures in order to learn from feedback. In Experiment 2, the picture category signaled the valence of the feedback. LD feedback pictures were associated with better memory and more recollective processing than shortly delayed ones. Feedback processing as reflected in the FRNpeak was attenuated for remembered as compared to forgotten LD feedback pictures. This suggests that when feedback was delayed, feedback processing and memory encoding competed for similar neural processing resources. As evidence by large FRNdiff amplitudes in the SD condition, the evaluation of shortly delayed feedback strongly relied on the procedural learning system. A complementary model-based single trial analysis was conducted to validate models of the functional significance of the FRN. Consistent with previous studies, feedback-locked N170 and P300 amplitudes were sensitive to feedback delay. Experiment 3 tested the hypothesis that the putative involvement of declarative learning processes in delayed feedback processing is mediated by the spontaneous generation of explicit outcome expectations during the feedback delay. A delayed feedback condition was compared with a Prediction condition in which participants were asked on each trial to predict the category of the upcoming feedback picture. Memory for the feedback pictures did not differ between the Prediction and Delay conditions. The FRNpeak subsequent memory effect obtained in Experiment 2 was replicated in both conditions, but more pronounced in the Prediction condition. As evidenced by ERP old/new effects, negative feedback pictures that disconfirmed explicit outcome expectations were associated with stronger recollective processing than those presented in the Delay condition. Positive feedback pictures elicited a recognition bias and increased familiarity signals in the memory test, which could reflect a generalization of reward value to pictures of the same category (indoor or outdoor scene). Taken together, the findings obtained in this dissertation show multiple ways by which feedback processing and memory encoding can interact, and how this interaction is shaped by feedback timing, valence, and explicit outcome expectations.


Feedbackbasiertes Lernen beruht auf einem prozeduralen Lernsystem, das auf der neurobiologischen Ebene durch dopaminerge Belohnungsvorhersagefehlersignale vermittelt wird. Studien mit bildgebenden Verfahren weisen darauf hin, dass die Verarbeitung von zeitlich verzögertem Feedback durch den Hippocampus unterstützt wird, eine Hirnstruktur, die mit deklarativen Gedächtnisprozessen assoziiert ist. Es ist jedoch noch nicht bekannt, wie die Verarbeitung von verzögertem Feedback mit der Gedächtnisenkodierung interagiert. In diesem Dissertationsprojekt wurde in einer Serie von drei Experimenten die Methode der nachfolgenden Erinnerung verwendet, um zu untersuchen, wie die inzidentelle Enkodierung von Feedbackbildern in einer probabilistischen Lernaufgabe sich auf das im ereigniskorrelierten Potenzial (EKP) messbare Korrelat von Belohnungsvorhersagefehlern in der Feedbackverarbeitung, die Feedback-Negativierung (FRN), auswirkt und wie diese Interaktion durch zeitliche Charakteristika und Valenz des Feedbacks sowie durch explizite Ergebniserwartungen moduliert wird. Im ersten Experiment wurden Bilder von Innenräumen und Landschaften zusammen mit dem Feedback in der Lernaufgabe präsentiert, wobei die Bilder nicht relevant für die Aufgabe waren. In der darauf folgenden Testphase wurde ein unerwarteter Rekognitionstest für die Bilder durchgeführt. FRN-Amplituden wurden in den während der Feedbackpräsentation aufgezeichneten EKP gemessen (FRNpeak), sowie in der Differenzwelle, die durch die Subtraktion der durch positives Feedback erzeugten EKP von den durch negatives Feedback erzeugten EKP gebildet wurde (FRNdiff). Beide FRN-Maße wurden für später erinnerte und später vergessene Bilder verglichen. Bilder, die zusammen mit positivem Feedback gezeigt wurden, wurden besser erinnert als solche, die mit negativem Feedback gepaart wurden, und EKP-Amplituden im Zeitfenster der FRNdiff prädizierten spätere Erinnerung ausschließlich für Bilder, die zusammen mit positivem Feedback präsentiert wurden. Übereinstimmend mit früheren Studien erzeugte kurz verzögertes Feedback (500 ms) größere FRNdiff-Amplituden als lang verzögertes Feedback (6500 ms), wohingegen das umgekehrte Muster für FRNpeak-Amplituden gefunden wurde. Wie durch behaviorale Maße und EKP-Alt/Neu-Effekte belegt, stärkte die Verarbeitung von positivem Feedback vor allem das vertrautheitsbasierte Erinnern der zeitgleich präsentierten Bilder, jedoch wirkten sich die zeitlichen Parameter der Feedbackpräsentation nicht auf das Gedächtnis aus, vermutlich weil eine Verarbeitung der Bilder nicht notwendig war, um das Feedback zum Lernen zu nutzen. Im zweiten Experiment wurde daher die Bildkategorie (Innenraum oder Landschaft), mit der Valenz des Feedbacks verknüpft. Lang verzögerte Feedbackbilder waren mit besserer Erinnerung und stärkerer rekollektiver Verarbeitung assoziiert als solche, die mit kurzer Verzögerung präsentiert worden waren. Die Feedbackverarbeitung, gemessen als FRNpeak-Amplitude, war geringer für lang verzögerte Feedbackbilder, die anschließend erinnert wurden als für solche, die nicht erinnert wurden. Dies legt nahe, dass die Verarbeitung von zeitlich verzögertem Feedback und die Gedächtnisenkodierung auf ähnliche neuronale Verarbeitungskapazitäten zugreifen. Wie anhand von FRNdiff-Amplituden ersichtlich, beruhte die Evaluation von zeitlich kurz verzögertem Feedback in starkem Ausmaß auf dem prozeduralen Lernsystem. Eine ergänzende, modellbasierte Analyse auf der Ebene einzelner Lerndurchgänge wurde durchgeführt, um Modelle der funktionalen Bedeutsamkeit der FRN zu validieren. Übereinstimmend mit vorherigen Studien wurden durch die Feedbackverarbeitung hervorgerufene N170- und P300-Amplituden durch die zeitliche Verzögerung des Feedbacks moduliert. Das dritte Experiment überprüfte die Hypothese, dass die mutmaßliche Beteiligung von deklarativen Lernprozessen bei der Verarbeitung von verzögertem Feedback durch die spontane Entwicklung expliziter Ergebniserwartungen während der Feedbackverzögerung vermittelt wird. Eine Bedingung mit verzögertem Feedback wurde mit einer Vorhersage-Bedingung kontrastiert, in der die Probanden in jedem Lerndurchgang die Kategorie des Feedbackbildes prädizierten. Die Erinnerung an die Feedbackbilder unterschied sich nicht zwischen den beiden Bedingungen. Der Effekt der nachfolgenden Erinnerung in den FRNpeak-Amplituden, der in Experiment 2 gefunden wurde, wurde in beiden Bedingungen repliziert, war jedoch in der Vorhersage-Bedingung stärker ausgeprägt. Wie durch EKP-Alt/Neu-Effekte belegt, waren negative Feedbackbilder, die die explizite Erwartung eines positiven Ergebnisses verletzten, mit einer stärkeren rekollektiven Verarbeitung verknüpft. Positive Bilder waren im Gedächtnistest mit besonders vielen falsch positiven Gedächtnisurteilen assoziiert, was mit einer Generalisierung des Belohnungswertes zu Bildern der gleichen Kategorie zusammenhängen könnte. Zusammengefasst zeigen die Ergebnisse dieser Dissertation, dass die Feedbackverarbeitung und die Gedächtnisenkodierung auf mehreren Wegen interagieren können. Die zeitlichen Charakteristika der Feedbackpräsentation, die Valenz des Feedbacks und explizite Ergebniserwartungen stellen wichtige Faktoren dar, die diese Interaktion beeinflussen.

@phdthesis{Höltje_Diss_2020,
title = {Interactions between immediate and delayed feedback processing and memory encoding: an investigation using event-related potentials},
author = {Gerrit H{\"o}ltje},
url = {https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/30348},
doi = {https://doi.org/https://dx.doi.org/10.22028/D291-32889},
year = {2020},
date = {2020},
school = {Saarland University},
address = {Saarbruecken, Germany},
abstract = {Feedback-based learning relies on a procedural learning system mediated by dopaminergic reward prediction error (RPE) signals. Recent neuroimaging research indicates that the processing of temporally delayed feedback is supported by the hippocampus, a brain structure associated with declarative memory processes, but it is still unknown how delayed feedback processing and memory encoding interact. In this dissertation project, in a series of three experiments, a subsequent memory paradigm was employed to investigate how the incidental encoding of feedback pictures in a probabilistic learning task affects the event-related potential (ERP) correlate of RPEs in feedback processing, i.e., the feedback-related negativity (FRN), and how this interaction is modulated by feedback timing, valence, and explicit outcome expectations. In Experiment 1, task-unrelated scene pictures were presented together with performance feedback in the learning task. In an ensuing test phase, a surprise recognition memory test for the pictures was conducted. FRN amplitudes measured in the feedback-locked ERPs recorded during the learning phase (FRNpeak) and in the negative minus positive feedback difference wave (FRNdiff) were compared for subsequently remembered and forgotten feedback pictures. Pictures were remembered better when presented together with positive than with negative feedback, and ERP amplitudes in the FRNdiff time window predicted subsequent memory only for positive feedback pictures. Consistent with previous studies, shortly delayed (SD, 500 ms) feedback elicited larger FRNdiff amplitudes than long delayed feedback (LD, 6500 ms), whereas the reverse pattern was found in FRNpeak amplitudes. As evidenced by behavioral estimates and ERP old/new effects, positive feedback enhanced memory by boosting familiarity-based recognition. However, feedback timing did not affect memory, presumably because participants did not need to process the scene pictures in order to learn from feedback. In Experiment 2, the picture category signaled the valence of the feedback. LD feedback pictures were associated with better memory and more recollective processing than shortly delayed ones. Feedback processing as reflected in the FRNpeak was attenuated for remembered as compared to forgotten LD feedback pictures. This suggests that when feedback was delayed, feedback processing and memory encoding competed for similar neural processing resources. As evidence by large FRNdiff amplitudes in the SD condition, the evaluation of shortly delayed feedback strongly relied on the procedural learning system. A complementary model-based single trial analysis was conducted to validate models of the functional significance of the FRN. Consistent with previous studies, feedback-locked N170 and P300 amplitudes were sensitive to feedback delay. Experiment 3 tested the hypothesis that the putative involvement of declarative learning processes in delayed feedback processing is mediated by the spontaneous generation of explicit outcome expectations during the feedback delay. A delayed feedback condition was compared with a Prediction condition in which participants were asked on each trial to predict the category of the upcoming feedback picture. Memory for the feedback pictures did not differ between the Prediction and Delay conditions. The FRNpeak subsequent memory effect obtained in Experiment 2 was replicated in both conditions, but more pronounced in the Prediction condition. As evidenced by ERP old/new effects, negative feedback pictures that disconfirmed explicit outcome expectations were associated with stronger recollective processing than those presented in the Delay condition. Positive feedback pictures elicited a recognition bias and increased familiarity signals in the memory test, which could reflect a generalization of reward value to pictures of the same category (indoor or outdoor scene). Taken together, the findings obtained in this dissertation show multiple ways by which feedback processing and memory encoding can interact, and how this interaction is shaped by feedback timing, valence, and explicit outcome expectations.


Feedbackbasiertes Lernen beruht auf einem prozeduralen Lernsystem, das auf der neurobiologischen Ebene durch dopaminerge Belohnungsvorhersagefehlersignale vermittelt wird. Studien mit bildgebenden Verfahren weisen darauf hin, dass die Verarbeitung von zeitlich verz{\"o}gertem Feedback durch den Hippocampus unterst{\"u}tzt wird, eine Hirnstruktur, die mit deklarativen Ged{\"a}chtnisprozessen assoziiert ist. Es ist jedoch noch nicht bekannt, wie die Verarbeitung von verz{\"o}gertem Feedback mit der Ged{\"a}chtnisenkodierung interagiert. In diesem Dissertationsprojekt wurde in einer Serie von drei Experimenten die Methode der nachfolgenden Erinnerung verwendet, um zu untersuchen, wie die inzidentelle Enkodierung von Feedbackbildern in einer probabilistischen Lernaufgabe sich auf das im ereigniskorrelierten Potenzial (EKP) messbare Korrelat von Belohnungsvorhersagefehlern in der Feedbackverarbeitung, die Feedback-Negativierung (FRN), auswirkt und wie diese Interaktion durch zeitliche Charakteristika und Valenz des Feedbacks sowie durch explizite Ergebniserwartungen moduliert wird. Im ersten Experiment wurden Bilder von Innenr{\"a}umen und Landschaften zusammen mit dem Feedback in der Lernaufgabe pr{\"a}sentiert, wobei die Bilder nicht relevant f{\"u}r die Aufgabe waren. In der darauf folgenden Testphase wurde ein unerwarteter Rekognitionstest f{\"u}r die Bilder durchgef{\"u}hrt. FRN-Amplituden wurden in den w{\"a}hrend der Feedbackpr{\"a}sentation aufgezeichneten EKP gemessen (FRNpeak), sowie in der Differenzwelle, die durch die Subtraktion der durch positives Feedback erzeugten EKP von den durch negatives Feedback erzeugten EKP gebildet wurde (FRNdiff). Beide FRN-Ma{\ss}e wurden f{\"u}r sp{\"a}ter erinnerte und sp{\"a}ter vergessene Bilder verglichen. Bilder, die zusammen mit positivem Feedback gezeigt wurden, wurden besser erinnert als solche, die mit negativem Feedback gepaart wurden, und EKP-Amplituden im Zeitfenster der FRNdiff pr{\"a}dizierten sp{\"a}tere Erinnerung ausschlie{\ss}lich f{\"u}r Bilder, die zusammen mit positivem Feedback pr{\"a}sentiert wurden. {\"U}bereinstimmend mit fr{\"u}heren Studien erzeugte kurz verz{\"o}gertes Feedback (500 ms) gr{\"o}{\ss}ere FRNdiff-Amplituden als lang verz{\"o}gertes Feedback (6500 ms), wohingegen das umgekehrte Muster f{\"u}r FRNpeak-Amplituden gefunden wurde. Wie durch behaviorale Ma{\ss}e und EKP-Alt/Neu-Effekte belegt, st{\"a}rkte die Verarbeitung von positivem Feedback vor allem das vertrautheitsbasierte Erinnern der zeitgleich pr{\"a}sentierten Bilder, jedoch wirkten sich die zeitlichen Parameter der Feedbackpr{\"a}sentation nicht auf das Ged{\"a}chtnis aus, vermutlich weil eine Verarbeitung der Bilder nicht notwendig war, um das Feedback zum Lernen zu nutzen. Im zweiten Experiment wurde daher die Bildkategorie (Innenraum oder Landschaft), mit der Valenz des Feedbacks verkn{\"u}pft. Lang verz{\"o}gerte Feedbackbilder waren mit besserer Erinnerung und st{\"a}rkerer rekollektiver Verarbeitung assoziiert als solche, die mit kurzer Verz{\"o}gerung pr{\"a}sentiert worden waren. Die Feedbackverarbeitung, gemessen als FRNpeak-Amplitude, war geringer f{\"u}r lang verz{\"o}gerte Feedbackbilder, die anschlie{\ss}end erinnert wurden als f{\"u}r solche, die nicht erinnert wurden. Dies legt nahe, dass die Verarbeitung von zeitlich verz{\"o}gertem Feedback und die Ged{\"a}chtnisenkodierung auf {\"a}hnliche neuronale Verarbeitungskapazit{\"a}ten zugreifen. Wie anhand von FRNdiff-Amplituden ersichtlich, beruhte die Evaluation von zeitlich kurz verz{\"o}gertem Feedback in starkem Ausma{\ss} auf dem prozeduralen Lernsystem. Eine erg{\"a}nzende, modellbasierte Analyse auf der Ebene einzelner Lerndurchg{\"a}nge wurde durchgef{\"u}hrt, um Modelle der funktionalen Bedeutsamkeit der FRN zu validieren. {\"U}bereinstimmend mit vorherigen Studien wurden durch die Feedbackverarbeitung hervorgerufene N170- und P300-Amplituden durch die zeitliche Verz{\"o}gerung des Feedbacks moduliert. Das dritte Experiment {\"u}berpr{\"u}fte die Hypothese, dass die mutma{\ss}liche Beteiligung von deklarativen Lernprozessen bei der Verarbeitung von verz{\"o}gertem Feedback durch die spontane Entwicklung expliziter Ergebniserwartungen w{\"a}hrend der Feedbackverz{\"o}gerung vermittelt wird. Eine Bedingung mit verz{\"o}gertem Feedback wurde mit einer Vorhersage-Bedingung kontrastiert, in der die Probanden in jedem Lerndurchgang die Kategorie des Feedbackbildes pr{\"a}dizierten. Die Erinnerung an die Feedbackbilder unterschied sich nicht zwischen den beiden Bedingungen. Der Effekt der nachfolgenden Erinnerung in den FRNpeak-Amplituden, der in Experiment 2 gefunden wurde, wurde in beiden Bedingungen repliziert, war jedoch in der Vorhersage-Bedingung st{\"a}rker ausgepr{\"a}gt. Wie durch EKP-Alt/Neu-Effekte belegt, waren negative Feedbackbilder, die die explizite Erwartung eines positiven Ergebnisses verletzten, mit einer st{\"a}rkeren rekollektiven Verarbeitung verkn{\"u}pft. Positive Bilder waren im Ged{\"a}chtnistest mit besonders vielen falsch positiven Ged{\"a}chtnisurteilen assoziiert, was mit einer Generalisierung des Belohnungswertes zu Bildern der gleichen Kategorie zusammenh{\"a}ngen k{\"o}nnte. Zusammengefasst zeigen die Ergebnisse dieser Dissertation, dass die Feedbackverarbeitung und die Ged{\"a}chtnisenkodierung auf mehreren Wegen interagieren k{\"o}nnen. Die zeitlichen Charakteristika der Feedbackpr{\"a}sentation, die Valenz des Feedbacks und explizite Ergebniserwartungen stellen wichtige Faktoren dar, die diese Interaktion beeinflussen.},
pubstate = {published},
type = {phdthesis}
}

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