Publications

Search methods are useful in hierarchical task network (HTN) planning to make performance less dependent on the domain knowledge provided, and to minimize plan costs. Here we investigate Monte-Carlo tree search (MCTS) as a new algorithmic alternative in HTN planning. We implement combinations of MCTS with heuristic search in Panda. We furthermore investigate MCTS in JSHOP, to address lifted (non-grounded) planning, leveraging the fact that, in contrast to other search methods, MCTS does not require a grounded task representation. Our new methods yield coverage performance on par with the state of the art, but in addition can effectively minimize plan cost over time.

• Wichlacz-SoCS2020     (0.55MB)
• https://ojs.aaai.org/index.php/SOCS/article/view/18538

@inproceedings{Wichlacz20MCTSSOCS,
title = {Applying Monte-Carlo Tree Search in HTN Planning},
author = {Julia Wichlacz and Daniel H{\"o}ller and {\'A}lvaro Torralba and J{\"o}rg Hoffmann},
url = {https://ojs.aaai.org/index.php/SOCS/article/view/18538},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 13th International Symposium on Combinatorial Search (SoCS)},
pages = {82-90},
publisher = {AAAI Press},
address = {Vienna, Austria},
abstract = {Search methods are useful in hierarchical task network (HTN) planning to make performance less dependent on the domain knowledge provided, and to minimize plan costs. Here we investigate Monte-Carlo tree search (MCTS) as a new algorithmic alternative in HTN planning. We implement combinations of MCTS with heuristic search in Panda. We furthermore investigate MCTS in JSHOP, to address lifted (non-grounded) planning, leveraging the fact that, in contrast to other search methods, MCTS does not require a grounded task representation. Our new methods yield coverage performance on par with the state of the art, but in addition can effectively minimize plan cost over time.},
pubstate = {published},
type = {inproceedings}
}

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In HTN planning, the hierarchy has a wide impact on solutions. First, there is (usually) no state-based goal given, the objective is given via the hierarchy. Second, it enforces actions to be in a plan. Third, planners are not allowed to add actions apart from those introduced via decomposition, i.e. via the hierarchy. However, no heuristic considers the interplay of hierarchy and actions in the plan exactly (without relaxation) because this makes heuristic calculation NP-hard even under delete relaxation. We introduce the problem class of delete- and ordering-free HTN planning as basis for novel HTN heuristics and show that its plan existence problem is still NP-complete. We then introduce heuristics based on the new class using an integer programming model to solve it.

• Höller-IJCAI-20     (0.3MB)
• https://www.ijcai.org/proceedings/2020/564
• https://doi.org/10.24963/ijcai.2020/564

@inproceedings{Hoeller2020IJCAI,
title = {Delete- and Ordering-Relaxation Heuristics for HTN Planning},
author = {Daniel H{\"o}ller and Pascal Bercher and Gregor Behnke},
url = {https://www.ijcai.org/proceedings/2020/564},
doi = {https://doi.org/10.24963/ijcai.2020/564},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI)},
pages = {4076-4083},
publisher = {IJCAI organization},
address = {Yokohama, Japan},
abstract = {In HTN planning, the hierarchy has a wide impact on solutions. First, there is (usually) no state-based goal given, the objective is given via the hierarchy. Second, it enforces actions to be in a plan. Third, planners are not allowed to add actions apart from those introduced via decomposition, i.e. via the hierarchy. However, no heuristic considers the interplay of hierarchy and actions in the plan exactly (without relaxation) because this makes heuristic calculation NP-hard even under delete relaxation. We introduce the problem class of delete- and ordering-free HTN planning as basis for novel HTN heuristics and show that its plan existence problem is still NP-complete. We then introduce heuristics based on the new class using an integer programming model to solve it.},
pubstate = {published},
type = {inproceedings}
}

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A long-standing question in language understanding is whether pragmatic inferences are effortful or whether they happen seamlessly without measurable cognitive effort. We here measure the strength of particularized pragmatic inferences in a setting with high vs. low cognitive load. Cognitive load is induced by a secondary dot tracking task.

If this type of pragmatic inference comes at no cognitive processing cost, inferences should be similarly strong in both the high and the low load condition. If they are effortful, we expect a smaller effect size in the dual tasking condition. Our results show that participants who have difficulty in dual tasking (as evidenced by incorrect answers to comprehension questions) exhibit a smaller pragmatic effect when they were distracted with a secondary task in comparison to the single task condition. This finding supports the idea that pragmatic inferences are effortful.

• cogsci20_proceedings_ryzhova     (0.8MB)
• https://www.semanticscholar.org/paper/Processing-particularized-pragmatic-inferences-load-Ryzhova-Demberg/a5b8d4c72590eaaf965d91d8fafa2495f680313d

@inproceedings{Ryzhova2020,
title = {Processing particularized pragmatic inferences under load},
author = {Margarita Ryzhova and Vera Demberg},
url = {https://www.semanticscholar.org/paper/Processing-particularized-pragmatic-inferences-load-Ryzhova-Demberg/a5b8d4c72590eaaf965d91d8fafa2495f680313d},
year = {2020},
date = {2020-10-17},
booktitle = {Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020)},
abstract = {A long-standing question in language understanding is whether pragmatic inferences are effortful or whether they happen seamlessly without measurable cognitive effort. We here measure the strength of particularized pragmatic inferences in a setting with high vs. low cognitive load. Cognitive load is induced by a secondary dot tracking task. If this type of pragmatic inference comes at no cognitive processing cost, inferences should be similarly strong in both the high and the low load condition. If they are effortful, we expect a smaller effect size in the dual tasking condition. Our results show that participants who have difficulty in dual tasking (as evidenced by incorrect answers to comprehension questions) exhibit a smaller pragmatic effect when they were distracted with a secondary task in comparison to the single task condition. This finding supports the idea that pragmatic inferences are effortful.},
pubstate = {published},
type = {inproceedings}
}

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The current study investigated how a contextual list signal influences comprehenders’ inference generation of upcoming discourse relations and whether individual differences in working memory capacity and linguistic experience influence the generation of these inferences. Participants were asked to complete two-sentence stories, the first sentence of which contained an expression of quantity (a few, multiple). Several individual-difference measures were calculated to explore whether individual characteristics can explain the sensitivity to the contextual list signal. The results revealed that participants were sensitive to a contextual list signal (i.e., they provided list continuations), and this sensitivity was modulated by the participants’ linguistic experience, as measured by an author recognition test. The results showed no evidence that working memory affected participants’ responses. These results extend prior research by showing that contextual signals influence participants’ coherence-relation-inference generation. Further, the results of the current study emphasize the importance of individual reader characteristics when it comes to coherence-relation inferences.

• https://www.tandfonline.com/doi/full/10.1080/0163853X.2020.1813492
• https://doi.org/10.1080/0163853X.2020.1813492

@article{Scholman2020,
title = {Individual differences in expecting coherence relations: Exploring the variability in sensitivity to contextual signals in discourse},
author = {Merel Scholman and Vera Demberg and Ted J. M. Sanders},
url = {https://www.tandfonline.com/doi/full/10.1080/0163853X.2020.1813492},
doi = {https://doi.org/10.1080/0163853X.2020.1813492},
year = {2020},
date = {2020-10-02},
journal = {Discourse Processes},
pages = {844-861},
volume = {57},
number = {10},
abstract = {The current study investigated how a contextual list signal influences comprehenders’ inference generation of upcoming discourse relations and whether individual differences in working memory capacity and linguistic experience influence the generation of these inferences. Participants were asked to complete two-sentence stories, the first sentence of which contained an expression of quantity (a few, multiple). Several individual-difference measures were calculated to explore whether individual characteristics can explain the sensitivity to the contextual list signal. The results revealed that participants were sensitive to a contextual list signal (i.e., they provided list continuations), and this sensitivity was modulated by the participants’ linguistic experience, as measured by an author recognition test. The results showed no evidence that working memory affected participants’ responses. These results extend prior research by showing that contextual signals influence participants’ coherence-relation-inference generation. Further, the results of the current study emphasize the importance of individual reader characteristics when it comes to coherence-relation inferences.},
pubstate = {published},
type = {article}
}

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Event‐related potentials (ERPs) provide a multidimensional and real‐time window into neurocognitive processing. The typical Waveform‐based Component Structure (WCS) approach to ERPs assesses the modulation pattern of components—systematic, reoccurring voltage fluctuations reflecting specific computational operations—by looking at mean amplitude in predetermined time‐windows.

This WCS approach, however, often leads to inconsistent results within as well as across studies. It has been argued that at least some inconsistencies may be reconciled by considering spatiotemporal overlap between components; that is, components may overlap in both space and time, and given their additive nature, this means that the WCS may fail to accurately represent its underlying latent component structure (LCS). We employ regression‐based ERP (rERP) estimation to extend traditional approaches with an additional layer of analysis, which enables the explicit modeling of the LCS underlying WCS. To demonstrate its utility, we incrementally derive an rERP analysis of a recent study on language comprehension with seemingly inconsistent WCS‐derived results.

Analysis of the resultant regression models allows one to derive an explanation for the WCS in terms of how relevant regression predictors combine in space and time, and crucially, how individual predictors may be mapped onto unique components in LCS, revealing how these spatiotemporally overlap in the WCS. We conclude that rERP estimation allows for investigating how scalp‐recorded voltages derive from the spatiotemporal combination of experimentally manipulated factors. Moreover, when factors can be uniquely mapped onto components, rERPs may offer explanations for seemingly inconsistent ERP waveforms at the level of their underlying latent component structure.

• Brouwer2020SplittingEvent     (3.73MB)
• https://onlinelibrary.wiley.com/doi/10.1111/ejn.14961
• https://doi.org/10.1111/ejn.14961

@article{Brouwer2020,
title = {Splitting event‐related potentials: Modeling latent components using regression‐based waveform estimation},
author = {Harm Brouwer and Francesca Delogu and Matthew W. Crocker},
url = {https://onlinelibrary.wiley.com/doi/10.1111/ejn.14961},
doi = {https://doi.org/10.1111/ejn.14961},
year = {2020},
date = {2020-09-08},
journal = {European Journal of Neuroscience},
abstract = {Event‐related potentials (ERPs) provide a multidimensional and real‐time window into neurocognitive processing. The typical Waveform‐based Component Structure (WCS) approach to ERPs assesses the modulation pattern of components—systematic, reoccurring voltage fluctuations reflecting specific computational operations—by looking at mean amplitude in predetermined time‐windows. This WCS approach, however, often leads to inconsistent results within as well as across studies. It has been argued that at least some inconsistencies may be reconciled by considering spatiotemporal overlap between components; that is, components may overlap in both space and time, and given their additive nature, this means that the WCS may fail to accurately represent its underlying latent component structure (LCS). We employ regression‐based ERP (rERP) estimation to extend traditional approaches with an additional layer of analysis, which enables the explicit modeling of the LCS underlying WCS. To demonstrate its utility, we incrementally derive an rERP analysis of a recent study on language comprehension with seemingly inconsistent WCS‐derived results. Analysis of the resultant regression models allows one to derive an explanation for the WCS in terms of how relevant regression predictors combine in space and time, and crucially, how individual predictors may be mapped onto unique components in LCS, revealing how these spatiotemporally overlap in the WCS. We conclude that rERP estimation allows for investigating how scalp‐recorded voltages derive from the spatiotemporal combination of experimentally manipulated factors. Moreover, when factors can be uniquely mapped onto components, rERPs may offer explanations for seemingly inconsistent ERP waveforms at the level of their underlying latent component structure.},
pubstate = {published},
type = {article}
}

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Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation. In this paper, we focus on embedding-based semantic spaces, exploiting departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. We use different views of the data {—} words, parts of speech, semantic tags and synsets {—} to track translationese. Our analysis shows that (i) semantic distances between original target language and translations into this target language can be detected using the notion of isomorphism, (ii) language family ties with characteristics similar to linguistically motivated phylogenetic trees can be inferred from the distances and (iii) with delexicalised embeddings exhibiting source-language interference most significantly, other levels of abstraction display the same tendency, indicating the lexicalised results to be not “just“ due to possible topic differences between original and translated texts. To the best of our knowledge, this is the first time departures from isomorphism between embedding spaces are used to track translationese.

• https://www.aclweb.org/anthology/2020.coling-main.532/
• https://doi.org/10.18653/v1/2020.coling-main.532

@inproceedings{DuttaEtal:COLING:2020,
title = {Understanding Translationese in Multi-view Embedding Spaces},
author = {Koel Dutta Chowdhury and Cristina Espa{\~n}a-Bonet and Josef van Genabith},
url = {https://www.aclweb.org/anthology/2020.coling-main.532/},
doi = {https://doi.org/10.18653/v1/2020.coling-main.532},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
pages = {6056-6062},
publisher = {International Committee on Computational Linguistics},
address = {Barcelona, Catalonia (Online)},
abstract = {Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation. In this paper, we focus on embedding-based semantic spaces, exploiting departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. We use different views of the data {---} words, parts of speech, semantic tags and synsets {---} to track translationese. Our analysis shows that (i) semantic distances between original target language and translations into this target language can be detected using the notion of isomorphism, (ii) language family ties with characteristics similar to linguistically motivated phylogenetic trees can be inferred from the distances and (iii) with delexicalised embeddings exhibiting source-language interference most significantly, other levels of abstraction display the same tendency, indicating the lexicalised results to be not “just“ due to possible topic differences between original and translated texts. To the best of our knowledge, this is the first time departures from isomorphism between embedding spaces are used to track translationese.},
pubstate = {published},
type = {inproceedings}
}

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Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages. However, recent works also showed that results from high-resource languages could not be easily transferred to realistic, low-resource scenarios. In this work, we study trends in performance for different amounts of available resources for the three African languages Hausa, isiXhosa and on both NER and topic classification. We show that in combination with transfer learning or distant supervision, these models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. However, we also find settings where this does not hold. Our discussions and additional experiments on assumptions such as time and hardware restrictions highlight challenges and opportunities in low-resource learning.

• https://www.aclweb.org/anthology/2020.emnlp-main.204
• https://doi.org/10.18653/v1/2020.emnlp-main.204

@inproceedings{hedderich-etal-2020-transfer,
title = {Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages},
author = {Michael Hedderich and David Adelani and Dawei Zhu and Alabi Jesujoba and Markus Udia and Dietrich Klakow},
url = {https://www.aclweb.org/anthology/2020.emnlp-main.204},
doi = {https://doi.org/10.18653/v1/2020.emnlp-main.204},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
pages = {2580-2591},
publisher = {Association for Computational Linguistics},
abstract = {Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages. However, recent works also showed that results from high-resource languages could not be easily transferred to realistic, low-resource scenarios. In this work, we study trends in performance for different amounts of available resources for the three African languages Hausa, isiXhosa and on both NER and topic classification. We show that in combination with transfer learning or distant supervision, these models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. However, we also find settings where this does not hold. Our discussions and additional experiments on assumptions such as time and hardware restrictions highlight challenges and opportunities in low-resource learning.},
pubstate = {published},
type = {inproceedings}
}

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Fine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline. At the same time, probing has emerged as a way to investigate the linguistic knowledge captured by pre-trained models. Very little is, however, understood about how fine-tuning affects the representations of pre-trained models and thereby the linguistic knowledge they encode. This paper contributes towards closing this gap. We study three different pre-trained models: BERT, RoBERTa, and ALBERT, and investigate through sentence-level probing how fine-tuning affects their representations. We find that for some probing tasks fine-tuning leads to substantial changes in accuracy, possibly suggesting that fine-tuning introduces or even removes linguistic knowledge from a pre-trained model. These changes, however, vary greatly across different models, fine-tuning and probing tasks. Our analysis reveals that while fine-tuning indeed changes the representations of a pre-trained model and these changes are typically larger for higher layers, only in very few cases, fine-tuning has a positive effect on probing accuracy that is larger than just using the pre-trained model with a strong pooling method. Based on our findings, we argue that both positive and negative effects of fine-tuning on probing require a careful interpretation.

• https://www.aclweb.org/anthology/2020.findings-emnlp.227
• https://doi.org/10.18653/v1/2020.findings-emnlp.227

@inproceedings{mosbach-etal-2020-interplay-fine,
title = {On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers},
author = {Marius Mosbach and Anna Khokhlova and Michael Hedderich and Dietrich Klakow},
url = {https://www.aclweb.org/anthology/2020.findings-emnlp.227},
doi = {https://doi.org/10.18653/v1/2020.findings-emnlp.227},
year = {2020},
date = {2020},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
pages = {2502-2516},
publisher = {Association for Computational Linguistics},
abstract = {Fine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline. At the same time, probing has emerged as a way to investigate the linguistic knowledge captured by pre-trained models. Very little is, however, understood about how fine-tuning affects the representations of pre-trained models and thereby the linguistic knowledge they encode. This paper contributes towards closing this gap. We study three different pre-trained models: BERT, RoBERTa, and ALBERT, and investigate through sentence-level probing how fine-tuning affects their representations. We find that for some probing tasks fine-tuning leads to substantial changes in accuracy, possibly suggesting that fine-tuning introduces or even removes linguistic knowledge from a pre-trained model. These changes, however, vary greatly across different models, fine-tuning and probing tasks. Our analysis reveals that while fine-tuning indeed changes the representations of a pre-trained model and these changes are typically larger for higher layers, only in very few cases, fine-tuning has a positive effect on probing accuracy that is larger than just using the pre-trained model with a strong pooling method. Based on our findings, we argue that both positive and negative effects of fine-tuning on probing require a careful interpretation.},
pubstate = {published},
type = {inproceedings}
}

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Speakers have several options when they express a discourse relation: they can leave it implicit, or make it explicit, usually through a connective. Although not all connectives can go with every relation, there is one that is particularly frequent and compatible with very many discourse relations, namely and. In this paper, we investigate the effect of discourse relation type and text genre on the production and perception of underspecified relations of contrast and consequence signalled by and. We combine a corpus study of spoken English, a production experiment and a perception experiment in order to test two hypotheses: (1) and is more compatible with relations of consequence than of contrast, due to factors of cognitive complexity and conceptual differences; (2) and is more compatible with informal than formal genres, because of requirements of recipient design. The three studies partially converge in identifying a stable effect of relation type and genre on the production and perception of underspecified relations of consequence and contrast marked by and.

• https://journals.openedition.org/discours/10848
• https://doi.org/10.4000/discours.10848

@article{Crible2020,
title = {When Do We Leave Discourse Relations Underspecified? The Effect of Formality and Relation Type},
author = {Ludivine Crible and Vera Demberg},
url = {https://journals.openedition.org/discours/10848},
doi = {https://doi.org/10.4000/discours.10848},
year = {2020},
date = {2020},
journal = {Discours},
number = {26},
abstract = {Speakers have several options when they express a discourse relation: they can leave it implicit, or make it explicit, usually through a connective. Although not all connectives can go with every relation, there is one that is particularly frequent and compatible with very many discourse relations, namely and. In this paper, we investigate the effect of discourse relation type and text genre on the production and perception of underspecified relations of contrast and consequence signalled by and. We combine a corpus study of spoken English, a production experiment and a perception experiment in order to test two hypotheses: (1) and is more compatible with relations of consequence than of contrast, due to factors of cognitive complexity and conceptual differences; (2) and is more compatible with informal than formal genres, because of requirements of recipient design. The three studies partially converge in identifying a stable effect of relation type and genre on the production and perception of underspecified relations of consequence and contrast marked by and.},
pubstate = {published},
type = {article}
}

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A large-scale interdisciplinary research collaboration at Saarland University (Crocker et al. 2016) investigates the hypothesis that language use may be driven by the optimal utilization of the communication channel. The information-theoretic concepts of entropy (Shannon, 1949) and surprisal (Hale 2001; Levy 2008) have gained in popularity due to their potential to predict human linguistic behavior. The underlying assumption is that there is a certain total amount of information contained in a message, which is distributed over the individual units constituting it. Capturing this distribution of information is the goal of surprisal-based modeling with the intention of predicting the processing effort experienced by humans upon encountering these units. The ease of processing linguistic material is thus correlated with its contextually determined predictability, which may be appropriately indexed by Shannon’s notion of information. Multilingualism pervasiveness suggests that human language competence is used quite robustly, taking on various types of information and employing multi-source compensatory and guessing strategies. While it is not realistic to require from every single person to master several languages, it is certainly beneficial to strive and promote a significantly higher degree of receptive skills facilitating the access to other languages. Taking advantage of linguistic similarity – genetic, typological or areal – is the key to acquiring such abilities as efficiently as possible. Awareness that linguistic structures known of a specific language apply to other varieties in which similar phenomena are detectable is indeed essential

• Avgustinova2020.Festschrift     (0.33MB)
• https://www.coli.uni-saarland.de/~tania/ta-pub/Avgustinova2020.Festschrift.pdf

@inbook{TAfestGA,
title = {Surprisal in Intercomprehension},
author = {Tania Avgustinova},
editor = {Milena Slavcheva and Kiril Simov and Petya Osenova and Svetla Boytcheva},
url = {https://www.coli.uni-saarland.de/~tania/ta-pub/Avgustinova2020.Festschrift.pdf},
year = {2020},
date = {2020},
booktitle = {Knowledge, Language, Models},
isbn = {978-954-452-062-5},
pages = {6-19},
publisher = {INCOMA Ltd.},
address = {Shoumen, Bulgaria},
abstract = {A large-scale interdisciplinary research collaboration at Saarland University (Crocker et al. 2016) investigates the hypothesis that language use may be driven by the optimal utilization of the communication channel. The information-theoretic concepts of entropy (Shannon, 1949) and surprisal (Hale 2001; Levy 2008) have gained in popularity due to their potential to predict human linguistic behavior. The underlying assumption is that there is a certain total amount of information contained in a message, which is distributed over the individual units constituting it. Capturing this distribution of information is the goal of surprisal-based modeling with the intention of predicting the processing effort experienced by humans upon encountering these units. The ease of processing linguistic material is thus correlated with its contextually determined predictability, which may be appropriately indexed by Shannon’s notion of information. Multilingualism pervasiveness suggests that human language competence is used quite robustly, taking on various types of information and employing multi-source compensatory and guessing strategies. While it is not realistic to require from every single person to master several languages, it is certainly beneficial to strive and promote a significantly higher degree of receptive skills facilitating the access to other languages. Taking advantage of linguistic similarity – genetic, typological or areal – is the key to acquiring such abilities as efficiently as possible. Awareness that linguistic structures known of a specific language apply to other varieties in which similar phenomena are detectable is indeed essential},
pubstate = {published},
type = {inbook}
}

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Contrary to the Gricean maxims of Quantity (Grice, 1975), it has been repeatedly shown that speakers often include redundant information in their utterances (over- specifications). Previous research on referential communication has long debated whether this redundancy is the result of speaker-internal or addressee-oriented processes, while it is also unclear whether referential redundancy hinders or facilitates comprehension. We present a bounded-rational account of referential redundancy, according to which any word in an utterance, even if it is redundant, can be beneficial to comprehension, to the extent that it facilitates the reduction of listeners’ uncertainty regarding the target referent in a co-present visual scene. Information-theoretic metrics, such as Shannon’s entropy (Shannon, 1948), were employed in order to quantify this uncertainty in bits of information, and gain an estimate of the cognitive effort related to referential processing. Under this account, speakers may, therefore, utilise redundant adjectives in order to reduce the visually-determined entropy (and thereby their listeners’ cognitive effort) more uniformly across their utterances. In a series of experiments, we examined both the comprehension and the production of over-specifications in complex visual contexts. Our findings are in line with the bounded-rational account. Specifically, we present evidence that: (a) in view of complex visual scenes, listeners’ processing and identification of the target referent may be facilitated by the use of redundant adjectives, as well as by a more uniform reduction of uncertainty across the utterance, and (b) that, while both speaker-internal and addressee-oriented processes are at play in the production of over-specifications, listeners’ processing concerns may also influence the encoding of redundant adjectives, at least for some speakers, who encode redundant adjectives more frequently when these adjectives contribute to a more uniform reduction of referential entropy.

• https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/29453
• https://doi.org/10.22028/D291-31436

@phdthesis{Tourtouri2020,
title = {Rational redundancy in situated communication},
author = {Elli Tourtouri},
url = {https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/29453},
doi = {https://doi.org/10.22028/D291-31436},
year = {2020},
date = {2020},
school = {Saarland University},
address = {Saarbr{\"u}cken},
abstract = {Contrary to the Gricean maxims of Quantity (Grice, 1975), it has been repeatedly shown that speakers often include redundant information in their utterances (over- specifications). Previous research on referential communication has long debated whether this redundancy is the result of speaker-internal or addressee-oriented processes, while it is also unclear whether referential redundancy hinders or facilitates comprehension. We present a bounded-rational account of referential redundancy, according to which any word in an utterance, even if it is redundant, can be beneficial to comprehension, to the extent that it facilitates the reduction of listeners’ uncertainty regarding the target referent in a co-present visual scene. Information-theoretic metrics, such as Shannon’s entropy (Shannon, 1948), were employed in order to quantify this uncertainty in bits of information, and gain an estimate of the cognitive effort related to referential processing. Under this account, speakers may, therefore, utilise redundant adjectives in order to reduce the visually-determined entropy (and thereby their listeners’ cognitive effort) more uniformly across their utterances. In a series of experiments, we examined both the comprehension and the production of over-specifications in complex visual contexts. Our findings are in line with the bounded-rational account. Specifically, we present evidence that: (a) in view of complex visual scenes, listeners’ processing and identification of the target referent may be facilitated by the use of redundant adjectives, as well as by a more uniform reduction of uncertainty across the utterance, and (b) that, while both speaker-internal and addressee-oriented processes are at play in the production of over-specifications, listeners’ processing concerns may also influence the encoding of redundant adjectives, at least for some speakers, who encode redundant adjectives more frequently when these adjectives contribute to a more uniform reduction of referential entropy.},
pubstate = {published},
type = {phdthesis}
}

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Automatic speech processing (ASP) is understood as covering word recognition, the processing of higher linguistic components (syntax, semantics, and pragmatics), and the processing of computational paralinguistics (CP), which deals with speaker states and traits. This chapter attempts to track the role of prosody in ASP from the word level up to CP. A short history of the field from 1980 to 2020 distinguishes the early years (until 2000)— when the prosodic contribution to the modelling of linguistic phenomena, such as accents, boundaries, syntax, semantics, and dialogue acts, was the focus—from the later years, when the focus shifted to paralinguistics; prosody ceased to be visible. Different types of predictor variables are addressed, among them high-performance power features as well as leverage features, which can also be employed in teaching and therapy.

• batliner_moebius_oup2020-3     (0.93MB)
• https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780198832232.001.0001/oxfordhb-9780198832232-e-42
• https://doi.org/10.1093/oxfordhb/9780198832232.013.42

@inbook{Batliner/Moebius:2020,
title = {Prosody in automatic speech processing},
author = {Anton Batliner and Bernd M{\"o}bius},
editor = {Carlos Gussenhoven and Aoju Chen},
url = {https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780198832232.001.0001/oxfordhb-9780198832232-e-42},
doi = {https://doi.org/10.1093/oxfordhb/9780198832232.013.42},
year = {2020},
date = {2020},
booktitle = {The Oxford Handbook of Language Prosody, Chap. 46},
isbn = {9780198832232},
pages = {633-645},
publisher = {Oxford University Press},
abstract = {Automatic speech processing (ASP) is understood as covering word recognition, the processing of higher linguistic components (syntax, semantics, and pragmatics), and the processing of computational paralinguistics (CP), which deals with speaker states and traits. This chapter attempts to track the role of prosody in ASP from the word level up to CP. A short history of the field from 1980 to 2020 distinguishes the early years (until 2000)— when the prosodic contribution to the modelling of linguistic phenomena, such as accents, boundaries, syntax, semantics, and dialogue acts, was the focus—from the later years, when the focus shifted to paralinguistics; prosody ceased to be visible. Different types of predictor variables are addressed, among them high-performance power features as well as leverage features, which can also be employed in teaching and therapy.},
pubstate = {published},
type = {inbook}
}

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The languages of Central and Eastern Europe addressed in this chapter form a typologically divergent collection that includes Slavic (Belarusian, Bulgarian, Czech, Macedonian, Polish, Russian, pluricentric Bosnian-Croatian-Montenegrin-Serbian, Slovak, Slovenian, Ukrainian), Baltic (Latvian, Lithuanian), Finno-Ugric (Hungarian, Finnish, Estonian), and Romance (Romanian). Their prosodic features and structures have been explored to various depths, from different theoretical perspectives, sometimes on the basis of relatively sparse material. Still, enough is known to see that their typological divergence as well as other factors contribute to vivid differences in their prosodic systems. While belonging to intonational languages, they differ in pitch patterns and their usage, duration, and rhythm (some involve phonological duration), as well as prominence mechanisms, accentuation, and word stress (fixed or mobile). Several languages in the area have what is referred to by different traditions as pitch accents, tones or syllable accents, or intonations.

• Karpinski_etal_2020     (0.21MB)
• https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780198832232.001.0001/oxfordhb-9780198832232-e-14

@inbook{Karpinski/etal:2020,
title = {Central and Eastern Europe},
author = {Maciej Karpiňski and Bistra Andreeva and Eva Liina Asu and Štefan Beňuš and Anna Daugavet and Katalin M{\'a}dy},
editor = {Carlos Gussenhoven and Aoju Chen},
url = {https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780198832232.001.0001/oxfordhb-9780198832232-e-14},
year = {2020},
date = {2020},
booktitle = {The Oxford Handbook of Language Prosody, Chap. 15},
isbn = {9780198832232},
pages = {225-235},
publisher = {Oxford University Press},
abstract = {The languages of Central and Eastern Europe addressed in this chapter form a typologically divergent collection that includes Slavic (Belarusian, Bulgarian, Czech, Macedonian, Polish, Russian, pluricentric Bosnian-Croatian-Montenegrin-Serbian, Slovak, Slovenian, Ukrainian), Baltic (Latvian, Lithuanian), Finno-Ugric (Hungarian, Finnish, Estonian), and Romance (Romanian). Their prosodic features and structures have been explored to various depths, from different theoretical perspectives, sometimes on the basis of relatively sparse material. Still, enough is known to see that their typological divergence as well as other factors contribute to vivid differences in their prosodic systems. While belonging to intonational languages, they differ in pitch patterns and their usage, duration, and rhythm (some involve phonological duration), as well as prominence mechanisms, accentuation, and word stress (fixed or mobile). Several languages in the area have what is referred to by different traditions as pitch accents, tones or syllable accents, or intonations.},
pubstate = {published},
type = {inbook}
}

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State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language. However, it is still unclear to what extent neural LID models generalize to speech samples with different acoustic conditions due to domain shift. In this paper, we present a set of experiments to investigate the impact of domain mismatch on the performance of neural LID systems for a subset of six Slavic languages across two domains (read speech and radio broadcast) and examine two low-level signal descriptors (spectral and cepstral features) for this task. Our experiments show that (1) out-of-domain speech samples severely hinder the performance of neural LID models, and (2) while both spectral and cepstral features show comparable performance within-domain, spectral features show more robustness under domain mismatch. Moreover, we apply unsupervised domain adaptation to minimize the discrepancy between the two domains in our study. We achieve relative accuracy improvements that range from 9% to 77% depending on the diversity of acoustic conditions in the source domain.

• https://arxiv.org/abs/2008.00545
• https://doi.org/10.21437/Interspeech.2020-2930

@inproceedings{abdullah_etal_is2020,
title = {Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages},
author = {Badr M. Abdullah and Tania Avgustinova and Bernd M{\"o}bius and Dietrich Klakow},
url = {https://arxiv.org/abs/2008.00545},
doi = {https://doi.org/10.21437/Interspeech.2020-2930},
year = {2020},
date = {2020},
booktitle = {Proceedings of Interspeech 2020},
pages = {477-481},
abstract = {State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language. However, it is still unclear to what extent neural LID models generalize to speech samples with different acoustic conditions due to domain shift. In this paper, we present a set of experiments to investigate the impact of domain mismatch on the performance of neural LID systems for a subset of six Slavic languages across two domains (read speech and radio broadcast) and examine two low-level signal descriptors (spectral and cepstral features) for this task. Our experiments show that (1) out-of-domain speech samples severely hinder the performance of neural LID models, and (2) while both spectral and cepstral features show comparable performance within-domain, spectral features show more robustness under domain mismatch. Moreover, we apply unsupervised domain adaptation to minimize the discrepancy between the two domains in our study. We achieve relative accuracy improvements that range from 9% to 77% depending on the diversity of acoustic conditions in the source domain.},
pubstate = {published},
type = {inproceedings}
}

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Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.

• https://www.aclweb.org/anthology/2020.vardial-1.12

@inproceedings{abdullah_etal_vardial2020,
title = {Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification},
author = {Badr M. Abdullah and Jacek Kudera and Tania Avgustinova and Bernd M{\"o}bius and Dietrich Klakow},
url = {https://www.aclweb.org/anthology/2020.vardial-1.12},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2020)},
pages = {128-139},
publisher = {International Committee on Computational Linguistics (ICCL)},
address = {Barcelona, Spain (Online)},
abstract = {Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.},
pubstate = {published},
type = {inproceedings}
}

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When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction. A novice user might need an object explained piece by piece, while for an expert, talking about the complex object (e.g. a wall or railing) directly may be more succinct and efficient. We show how to generate building instructions at different levels of abstraction in Minecraft. We introduce the use of hierarchical planning to this end, a method from AI planning which can capture the structure of complex objects neatly. A crowdsourcing evaluation shows that the choice of abstraction level matters to users, and that an abstraction strategy which balances low-level and high-level object descriptions compares favorably to ones which don’t.

• 2020.coling-main.252     (0.77MB)
• https://aclanthology.org/2020.coling-main.252/
• https://doi.org/10.18653/v1/2020.coling-main.252

@inproceedings{kohn-etal-2020-generating,
title = {Generating Instructions at Different Levels of Abstraction},
author = {Arne K{\"o}hn and Julia Wichlacz and {\'A}lvaro Torralba and Daniel H{\"o}ller and J{\"o}rg Hoffmann and Alexander Koller},
url = {https://aclanthology.org/2020.coling-main.252/},
doi = {https://doi.org/10.18653/v1/2020.coling-main.252},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
pages = {2802-2813},
publisher = {International Committee on Computational Linguistics},
address = {Barcelona, Spain (Online)},
abstract = {When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction. A novice user might need an object explained piece by piece, while for an expert, talking about the complex object (e.g. a wall or railing) directly may be more succinct and efficient. We show how to generate building instructions at different levels of abstraction in Minecraft. We introduce the use of hierarchical planning to this end, a method from AI planning which can capture the structure of complex objects neatly. A crowdsourcing evaluation shows that the choice of abstraction level matters to users, and that an abstraction strategy which balances low-level and high-level object descriptions compares favorably to ones which don't.},
pubstate = {published},
type = {inproceedings}
}

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We present a comprehensive platform to run human-computer experiments where an agent instructs a human in Minecraft, a 3D blocksworld environment. This platform enables comparisons between different agents by matching users to agents. It performs extensive logging and takes care of all boilerplate, allowing to easily incorporate new agents to evaluate them. Our environment is prepared to evaluate any kind of instruction giving system, recording the interaction and all actions of the user. We provide example architects, a Wizard-of-Oz architect and set-up scripts to automatically download, build and start the platform.

• 2020.sigdial-1.7     (0.64MB)
• https://www.aclweb.org/anthology/2020.sigdial-1.7

@inproceedings{Hoeller2020IJCAIb,
title = {MC-Saar-Instruct: a Platform for Minecraft Instruction Giving Agents},
author = {Arne K{\"o}hn and Julia Wichlacz and Christine Sch{\"a}fer and {\'A}lvaro Torralba and J{\"o}rg Hoffmann and Alexander Koller},
url = {https://www.aclweb.org/anthology/2020.sigdial-1.7},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue},
pages = {53-56},
publisher = {Association for Computational Linguistics},
address = {1st virtual meeting},
abstract = {We present a comprehensive platform to run human-computer experiments where an agent instructs a human in Minecraft, a 3D blocksworld environment. This platform enables comparisons between different agents by matching users to agents. It performs extensive logging and takes care of all boilerplate, allowing to easily incorporate new agents to evaluate them. Our environment is prepared to evaluate any kind of instruction giving system, recording the interaction and all actions of the user. We provide example architects, a Wizard-of-Oz architect and set-up scripts to automatically download, build and start the platform.},
pubstate = {published},
type = {inproceedings}
}

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To make planning feasible, planning models abstract from many details of the modeled system. When executing plans in the actual system, the model might be inaccurate in a critical point, and plan execution may fail. There are two options to handle this case: the previous solution can be modified to address the failure (plan repair), or the planning process can be re-started from the new situation (re-planning). In HTN planning, discarding the plan and generating a new one from the novel situation is not easily possible, because the HTN solution criteria make it necessary to take already executed actions into account. Therefore all approaches to repair plans in the literature are based on specialized algorithms. In this paper, we discuss the problem in detail and introduce a novel approach that makes it possible to use unchanged, off-the-shelf HTN planning systems to repair broken HTN plans. That way, no specialized solvers are needed.

• 2020-Hoeller-KI     (0.36MB)
• https://link.springer.com/chapter/10.1007/978-3-030-58285-2_7

@inproceedings{Hoeller2020KI,
title = {HTN Plan Repair via Model Transformation},
author = {Daniel H{\"o}ller and Pascal Bercher and Gregor Behnke and Susanne Biundo},
url = {https://link.springer.com/chapter/10.1007/978-3-030-58285-2_7},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 43rd German Conference on Artificial Intelligence (KI)},
pages = {88-101},
publisher = {Springer},
abstract = {To make planning feasible, planning models abstract from many details of the modeled system. When executing plans in the actual system, the model might be inaccurate in a critical point, and plan execution may fail. There are two options to handle this case: the previous solution can be modified to address the failure (plan repair), or the planning process can be re-started from the new situation (re-planning). In HTN planning, discarding the plan and generating a new one from the novel situation is not easily possible, because the HTN solution criteria make it necessary to take already executed actions into account. Therefore all approaches to repair plans in the literature are based on specialized algorithms. In this paper, we discuss the problem in detail and introduce a novel approach that makes it possible to use unchanged, off-the-shelf HTN planning systems to repair broken HTN plans. That way, no specialized solvers are needed.},
pubstate = {published},
type = {inproceedings}
}

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Language processing is predictive in nature. But how do people balance multiple competing options as they predict upcoming meanings? Here, we investigated whether readers generate graded predictions about grammatical gender of nouns. Sentence contexts were manipulated so that they strongly biased people’s expectations towards two or more nouns that had the same grammatical gender (single bias condition), or they biased multiple genders from different grammatical classes (multiple bias condition). Our expectation was that unexpected articles should lead to elevated reading times (RTs) in the single-bias condition when probabilistic expectations towards a particular gender are violated. Indeed, the results showed greater sensitivity among language users towards unexpected articles in the single-bias condition, however, RTs on unexpected gendermarked articles were facilitated, and not slowed. Our data confirm that difficulty in sentence processing is modulated by uncertainty about predicted information, and suggest that readers make graded predictions about grammatical gender.

• https://link.springer.com/article/10.3758/s13415-015-0340-0
• https://doi.org/10.3758/s13415-015-0340-0

@inproceedings{haeuser2020great,
title = {Great expectations: Evidence for graded prediction of grammatical gender},
author = {Katja H{\"a}user and Jutta Kray and Arielle Borovsky},
url = {https://link.springer.com/article/10.3758/s13415-015-0340-0},
doi = {https://doi.org/10.3758/s13415-015-0340-0},
year = {2020},
date = {2020},
booktitle = {CogSci},
abstract = {Language processing is predictive in nature. But how do people balance multiple competing options as they predict upcoming meanings? Here, we investigated whether readers generate graded predictions about grammatical gender of nouns. Sentence contexts were manipulated so that they strongly biased people's expectations towards two or more nouns that had the same grammatical gender (single bias condition), or they biased multiple genders from different grammatical classes (multiple bias condition). Our expectation was that unexpected articles should lead to elevated reading times (RTs) in the single-bias condition when probabilistic expectations towards a particular gender are violated. Indeed, the results showed greater sensitivity among language users towards unexpected articles in the single-bias condition, however, RTs on unexpected gendermarked articles were facilitated, and not slowed. Our data confirm that difficulty in sentence processing is modulated by uncertainty about predicted information, and suggest that readers make graded predictions about grammatical gender.},
pubstate = {published},
type = {inproceedings}
}

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Automatically generated stories need to be not only coherent, but also interesting. Apart from realizing a story line, the text also needs to include rich details to engage the readers. We propose a model that features two different generation components: an outliner, which proceeds the main story line to realize global coherence; a detailer, which supplies relevant details to the story in a locally coherent manner. Human evaluations show our model substantially improves the informativeness of generated text while retaining its coherence, outperforming various baselines.

• https://www.aclweb.org/anthology/2020.coling-main.212
• https://doi.org/10.18653/v1/2020.coling-main.212

@inproceedings{zhai-etal-2020-story,
title = {Story Generation with Rich Details},
author = {Fangzhou Zhai and Vera Demberg and Alexander Koller},
url = {https://www.aclweb.org/anthology/2020.coling-main.212},
doi = {https://doi.org/10.18653/v1/2020.coling-main.212},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics (CoLing 2020)},
pages = {2346-2351},
publisher = {International Committee on Computational Linguistics},
address = {Barcelona, Spain (Online)},
abstract = {Automatically generated stories need to be not only coherent, but also interesting. Apart from realizing a story line, the text also needs to include rich details to engage the readers. We propose a model that features two different generation components: an outliner, which proceeds the main story line to realize global coherence; a detailer, which supplies relevant details to the story in a locally coherent manner. Human evaluations show our model substantially improves the informativeness of generated text while retaining its coherence, outperforming various baselines.},
pubstate = {published},
type = {inproceedings}
}

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