Publications

• https://cognitivesciencesociety.org/cogsci-2022/

@inproceedings{Kravtchenko_2022_atypicality,
title = {Modeling atypicality inferences in pragmatic reasoning},
author = {Ekaterina Kravtchenko and Vera Demberg},
url = {https://cognitivesciencesociety.org/cogsci-2022/},
year = {2022},
date = {2022},
publisher = {CogSci 2022},
address = {Toronto, Canada},
pubstate = {published},
type = {inproceedings}
}

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• https://lrec2022.lrec-conf.org/en/?msclkid=daf2b7e9c0e011ec9f5e548d0b8ea2f3

@inproceedings{Fawzi_2022_Syntactic,
title = {Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German},
author = {Marie-Pauline Krielke and Luigi Talamo andM. Fawzi and J. Knappen},
url = {https://lrec2022.lrec-conf.org/en/?msclkid=daf2b7e9c0e011ec9f5e548d0b8ea2f3},
year = {2022},
date = {2022},
publisher = {LREC 2022},
address = {Marseille, France},
pubstate = {forthcoming},
type = {inproceedings}
}

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The prosodic word (PW) has been proposed as a planning unit in speech production (Levelt et al. [1999. A theory of lexical access in speech production. Behavioral and Brain Sciences, 22, 1–75]), supported by evidence that speech initiation time (RT) is faster for Dutch utterances with fewer PWs due to cliticisation (with the number of lexical words and syllables kept constant) (Wheeldon & Lahiri [1997. Prosodic units in speech production. Journal of Memory and Language, 37(3), 356–381. https://doi.org/10.1006/jmla.1997.2517], W&L). The present study examined prosodic cliticisation (and resulting RT) for a different set of potential clitics (articles, direct-object pronouns), in English, using a different response task (immediate reading aloud). W&L’s result of shorter RTs for fewer PWs was replicated for articles, but not for pronouns, suggesting a difference in cliticisation for these two function word types. However, a post-hoc analysis of the duration of the verb preceding the clitic suggests that both are cliticised. These findings highlight the importance of supplementing production latency measures with phonetic duration measures to understand different stages of language production during utterance planning.

• https://www.tandfonline.com/eprint/4K7DVYQIWRKITU3JCACY/full?target=10.1080/23273798.2022.2060517
• https://doi.org/10.1080/23273798.2022.2060517

@article{Yuen_of_2022,
title = {Planning of prosodic clitics in Australian English},
author = {Ivan Yuen and Katherine Demuth and Stefanie Shattuck-Hufnagel},
url = {https://www.tandfonline.com/eprint/4K7DVYQIWRKITU3JCACY/full?target=10.1080/23273798.2022.2060517},
doi = {https://doi.org/10.1080/23273798.2022.2060517},
year = {2022},
date = {2022-04-05},
journal = {Language, Cognition and Neuroscience},
pages = {1-6},
publisher = {Routledge},
abstract = {The prosodic word (PW) has been proposed as a planning unit in speech production (Levelt et al. [1999. A theory of lexical access in speech production. Behavioral and Brain Sciences, 22, 1–75]), supported by evidence that speech initiation time (RT) is faster for Dutch utterances with fewer PWs due to cliticisation (with the number of lexical words and syllables kept constant) (Wheeldon & Lahiri [1997. Prosodic units in speech production. Journal of Memory and Language, 37(3), 356–381. https://doi.org/10.1006/jmla.1997.2517], W&L). The present study examined prosodic cliticisation (and resulting RT) for a different set of potential clitics (articles, direct-object pronouns), in English, using a different response task (immediate reading aloud). W&L’s result of shorter RTs for fewer PWs was replicated for articles, but not for pronouns, suggesting a difference in cliticisation for these two function word types. However, a post-hoc analysis of the duration of the verb preceding the clitic suggests that both are cliticised. These findings highlight the importance of supplementing production latency measures with phonetic duration measures to understand different stages of language production during utterance planning.},
pubstate = {published},
type = {article}
}

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Abstract: We present results of an eye tracking experiment which aimed at testing sentence comprehension in closely related Slavic languages. Since none of the participants were trained in translation studies or Slavic linguistics, the study illustrates effects of intercomprehension. The participants were exposed to auditory stimuli in Bulgarian, Czech, Polish, and Russian accompanied by a visual scene. The analysis of anticipatory eye movements has shown that native speakers of one Slavic language listening to sentences in another Slavic language, turn their attention to and begin fixating on the referent objects as soon as they identify a predicate. This experiment provides evidence for surprisal-based effects in intercomprehension.

@inproceedings{Kudera/etal:2022a,
title = {Comprehension of closely related languages: A visual world eye tracking study},
author = {Jacek Kudera and Philip Georgis and Hasan Md Tusfiqur Alam and Bernd M{\"o}bius and Tania Avgustinova and Dietrich Klakow},
year = {2022},
date = {2022},
booktitle = {Elektronische Sprachsignalverarbeitung 2022, Tagungsband der 33. Konferenz (Sønderborg)},
pages = {212-219},
abstract = {Abstract: We present results of an eye tracking experiment which aimed at testing sentence comprehension in closely related Slavic languages. Since none of the participants were trained in translation studies or Slavic linguistics, the study illustrates effects of intercomprehension. The participants were exposed to auditory stimuli in Bulgarian, Czech, Polish, and Russian accompanied by a visual scene. The analysis of anticipatory eye movements has shown that native speakers of one Slavic language listening to sentences in another Slavic language, turn their attention to and begin fixating on the referent objects as soon as they identify a predicate. This experiment provides evidence for surprisal-based effects in intercomprehension.},
pubstate = {published},
type = {inproceedings}
}

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Abstract: Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space. In addition to their speech technology applications, AWE models have been shown to predict human performance on a variety of auditory lexical processing tasks. Current AWE models are based on neural networks and trained in a bottom-up approach that integrates acoustic cues to build up a word representation given an acoustic or symbolic supervision signal. Therefore, these models do not leverage or capture high-level lexical knowledge during the learning process. In this paper, we propose a multi-task learning model that incorporates top-down lexical knowledge into the training procedure of AWEs. Our model learns a mapping between the acoustic input and a lexical representation that encodes high-level information such as word semantics in addition to bottom-up form-based supervision. We experiment with three languages and demonstrate that incorporating lexical knowledge improves the embedding space discriminability and encourages the model to better separate lexical categories.

• https://www.isca-speech.org/archive/interspeech_2022/abdullah22_interspeech.html
• https://doi.org/´10.21437/Interspeech.2022-626

@inproceedings{Abdullah/etal:2022a,
title = {Integrating form and meaning: A multi-task learning model for acoustic word embeddings},
author = {Badr M. Abdullah and Bernd M{\"o}bius and Dietrich Klakow},
url = {https://www.isca-speech.org/archive/interspeech_2022/abdullah22_interspeech.html},
doi = {https://doi.org/´10.21437/Interspeech.2022-626},
year = {2022},
date = {2022},
booktitle = {Proceedings of Interspeech 2022},
pages = {1876-1880},
abstract = {Abstract: Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space. In addition to their speech technology applications, AWE models have been shown to predict human performance on a variety of auditory lexical processing tasks. Current AWE models are based on neural networks and trained in a bottom-up approach that integrates acoustic cues to build up a word representation given an acoustic or symbolic supervision signal. Therefore, these models do not leverage or capture high-level lexical knowledge during the learning process. In this paper, we propose a multi-task learning model that incorporates top-down lexical knowledge into the training procedure of AWEs. Our model learns a mapping between the acoustic input and a lexical representation that encodes high-level information such as word semantics in addition to bottom-up form-based supervision. We experiment with three languages and demonstrate that incorporating lexical knowledge improves the embedding space discriminability and encourages the model to better separate lexical categories.},
pubstate = {published},
type = {inproceedings}
}

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Abstract: This study compares how American (US) and German (DE) listeners perceive emotional expressiveness from Amazon Alexa text-to-speech (TTS) and human voices. Participants heard identical stimuli, manipulated from an emotionally ‘neutral‘ production to three levels of increased happiness generated by resynthesis. Results show that, for both groups, ‘happiness‘ manipulations lead to higher ratings of emotional valence (i.e., more positive) for the human voice. Moreover, there was a difference across the groups in their perception of arousal (i.e., excitement): US listeners show higher ratings for human voices with manipulations, while DE listeners perceive the Alexa voice as sounding less ‘excited‘ overall. We discuss these findings in terms of theories of cross-cultural emotion perception and human-computer interaction.

• https://www.isca-speech.org/archive/interspeech_2022/gessinger22_interspeech.html
• https://doi.org/10.21437/Interspeech.2022-146

@inproceedings{Gessinger/etal:2022a,
title = {Cross-cultural comparison of gradient emotion perception: Human vs. Alexa TTS voices},
author = {Iona Gessinger and Michelle Cohn and Georgia Zellou and Bernd M{\"o}bius},
url = {https://www.isca-speech.org/archive/interspeech_2022/gessinger22_interspeech.html},
doi = {https://doi.org/10.21437/Interspeech.2022-146},
year = {2022},
date = {2022},
booktitle = {Proceedings of Interspeech 2022},
pages = {4970-4974},
abstract = {Abstract: This study compares how American (US) and German (DE) listeners perceive emotional expressiveness from Amazon Alexa text-to-speech (TTS) and human voices. Participants heard identical stimuli, manipulated from an emotionally ‘neutral' production to three levels of increased happiness generated by resynthesis. Results show that, for both groups, ‘happiness' manipulations lead to higher ratings of emotional valence (i.e., more positive) for the human voice. Moreover, there was a difference across the groups in their perception of arousal (i.e., excitement): US listeners show higher ratings for human voices with manipulations, while DE listeners perceive the Alexa voice as sounding less ‘excited' overall. We discuss these findings in terms of theories of cross-cultural emotion perception and human-computer interaction.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.sciencedirect.com/journal/journal-of-phonetics/special-issue/10QQ0PG7696

@miscellaneous{Pardo/etal_2022b,
title = {Vocal Accommodation in Speech Communication},
author = {Jennifer Pardo and Elisa Pellegrino and Volker Dellwo and Bernd M{\"o}bius},
url = {https://www.sciencedirect.com/journal/journal-of-phonetics/special-issue/10QQ0PG7696},
year = {2022},
date = {2022},
booktitle = {Special issue of Journal of Phonetics: Vocal accommodation in speech communication, Volume 95},
pubstate = {published},
type = {miscellaneous}
}

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Abstract: This introductory article for the Special Issue on Vocal Accommodation in Speech Communication provides an overview of prevailing theories of vocal accommodation and summarizes the ten papers in the collection. Communication Accommodation Theory focusses on social factors evoking accent convergence or divergence, while the Interactive Alignment Model proposes cognitive integration of perception and production as an automatic priming mechanism driving convergence language production. Recent research including most of the papers in this Special Issue indicates that a hybrid or interactive synergy model provides a more comprehensive account of observed patterns of phonetic convergence than purely automatic mechanisms. Some of the fundamental questions that this special collection aimed to cover concerned (1) the nature of vocal accommodation in terms of underlying mechanisms and social functions in human–human and human–computer interaction; (2) the effect of task-specific and talker-specific characteristics (gender, age, personality, linguistic and cultural background, role in interaction) on degree and direction of convergence towards human and computer interlocutors; (3) integration of articulatory, perceptual, neurocognitive, and/or multimodal data to the analysis of acoustic accommodation in interactive and non-interactive speech tasks; and (4) the contribution of short/long-term accommodation in human–human and human–computer interactions to the diffusion of linguistic innovation and ultimately language variation and change.

• https://www.sciencedirect.com/science/article/abs/pii/S0095447022000717

@article{Pardo_etal22,
title = {Special issue: Vocal accommodation in speech communication},
author = {Jennifer Pardo and Elisa Pellegrino and Volker Dellwo and Bernd M{\"o}bius},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0095447022000717},
year = {2022},
date = {2022},
journal = {Journal of Phonetics},
pages = {paper 101196},
volume = {95, 1-9},
abstract = {Abstract: This introductory article for the Special Issue on Vocal Accommodation in Speech Communication provides an overview of prevailing theories of vocal accommodation and summarizes the ten papers in the collection. Communication Accommodation Theory focusses on social factors evoking accent convergence or divergence, while the Interactive Alignment Model proposes cognitive integration of perception and production as an automatic priming mechanism driving convergence language production. Recent research including most of the papers in this Special Issue indicates that a hybrid or interactive synergy model provides a more comprehensive account of observed patterns of phonetic convergence than purely automatic mechanisms. Some of the fundamental questions that this special collection aimed to cover concerned (1) the nature of vocal accommodation in terms of underlying mechanisms and social functions in human–human and human–computer interaction; (2) the effect of task-specific and talker-specific characteristics (gender, age, personality, linguistic and cultural background, role in interaction) on degree and direction of convergence towards human and computer interlocutors; (3) integration of articulatory, perceptual, neurocognitive, and/or multimodal data to the analysis of acoustic accommodation in interactive and non-interactive speech tasks; and (4) the contribution of short/long-term accommodation in human–human and human–computer interactions to the diffusion of linguistic innovation and ultimately language variation and change.},
pubstate = {published},
type = {article}
}

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Abstract: Planning models are usually defined in lifted, i.e. first order formalisms, while most solvers need (variable-free) grounded representations. Though techniques for grounding prune unnecessary parts of the model, grounding might – nevertheless – be prohibitively expensive in terms of runtime. To overcome this issue, there has been renewed interest in solving planning problems based on the lifted representation in the last years. While these approaches are based on (heuristic) search, we present an encoding of lifted classical planning in propositional logic and use SAT solvers to solve it. Our evaluation shows that our approach is competitive with the heuristic search-based approaches in satisficing planning and outperforms them in a (length-)optimal setting.

• https://ojs.aaai.org/index.php/ICAPS/article/view/19794

@inproceedings{HoellerB22,
title = {Encoding Lifted Classical Planning in Propositional Logic},
author = {Daniel H{\"o}ller and Gregor Behnke},
url = {https://ojs.aaai.org/index.php/ICAPS/article/view/19794},
year = {2022},
date = {2022},
booktitle = {Proceedings of the 32nd International Conference on Automated Planning and Scheduling (ICAPS)},
pages = {134-144},
publisher = {AAAI Press},
abstract = {Abstract: Planning models are usually defined in lifted, i.e. first order formalisms, while most solvers need (variable-free) grounded representations. Though techniques for grounding prune unnecessary parts of the model, grounding might – nevertheless – be prohibitively expensive in terms of runtime. To overcome this issue, there has been renewed interest in solving planning problems based on the lifted representation in the last years. While these approaches are based on (heuristic) search, we present an encoding of lifted classical planning in propositional logic and use SAT solvers to solve it. Our evaluation shows that our approach is competitive with the heuristic search-based approaches in satisficing planning and outperforms them in a (length-)optimal setting.},
pubstate = {published},
type = {inproceedings}
}

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Abstract:

Obtaining linguistic annotation from novice crowdworkers is far from trivial. A case in point is the annotation of discourse relations, which is a complicated task. Recent methods have obtained promising results by extracting relation labels from either discourse connectives (DCs) or question-answer (QA) pairs that participants provide. The current contribution studies the effect of worker selection and training on the agreement on implicit relation labels between workers and gold labels, for both the DC and the QA method. In Study 1, workers were not specifically selected or trained, and the results show that there is much room for improvement. Study 2 shows that a combination of selection and training does lead to improved results, but the method is cost- and time-intensive. Study 3 shows that a selection-only approach is a viable alternative; it results in annotations of comparable quality compared to annotations from trained participants. The results generalized over both the DC and QA method and therefore indicate that a selection-only approach could also be effective for other crowdsourced discourse annotation tasks.

• https://aclanthology.org/2022.lrec-1.231/

@inproceedings{ Scholmanet-al22-3,
title = {Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training.},
author = {Merel Scholman and Valentina Pyatkin and Frances Pik Yu Yung and Ido Dagan and Reut Tsarfaty and Vera Demberg},
url = {https://aclanthology.org/2022.lrec-1.231/},
year = {2022},
date = {2022},
booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France},
pages = {2148–2156},
publisher = {European Language Resources Association},
abstract = {Abstract: Obtaining linguistic annotation from novice crowdworkers is far from trivial. A case in point is the annotation of discourse relations, which is a complicated task. Recent methods have obtained promising results by extracting relation labels from either discourse connectives (DCs) or question-answer (QA) pairs that participants provide. The current contribution studies the effect of worker selection and training on the agreement on implicit relation labels between workers and gold labels, for both the DC and the QA method. In Study 1, workers were not specifically selected or trained, and the results show that there is much room for improvement. Study 2 shows that a combination of selection and training does lead to improved results, but the method is cost- and time-intensive. Study 3 shows that a selection-only approach is a viable alternative; it results in annotations of comparable quality compared to annotations from trained participants. The results generalized over both the DC and QA method and therefore indicate that a selection-only approach could also be effective for other crowdsourced discourse annotation tasks.},
pubstate = {published},
type = {inproceedings}
}

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Abstract: We present DiscoGeM, a crowdsourced corpus of 6,505 implicit discourse relations from three genres: political speech, literature, and encyclopedic texts. Each instance was annotated by 10 crowd workers. Various label aggregation methods were explored to evaluate how to obtain a label that best captures the meaning inferred by the crowd annotators. The results show that a significant proportion of discourse relations in DiscoGeM are ambiguous and can express multiple relation senses. Probability distribution labels better capture these interpretations than single labels. Further, the results emphasize that text genre crucially affects the distribution of discourse relations, suggesting that genre should be included as a factor in automatic relation classification. We make available the newly created DiscoGeM corpus, as well as the dataset with all annotator-level labels. Both the corpus and the dataset can facilitate a multitude of applications and research purposes, for example to function as training data to improve the performance of automatic discourse relation parsers, as well as facilitate research into non-connective signals of discourse relations.

• https://aclanthology.org/2022.lrec-1.351/

@article{Scholman_et-al22.2,
title = {DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations},
author = {Merel Scholman and Tianai Dong and Frances Pik Yu Yung and Vera Demberg},
url = {https://aclanthology.org/2022.lrec-1.351/},
year = {2022},
date = {2022},
journal = {Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 22), Marseille, France},
pages = {3281-3290},
abstract = {Abstract: We present DiscoGeM, a crowdsourced corpus of 6,505 implicit discourse relations from three genres: political speech, literature, and encyclopedic texts. Each instance was annotated by 10 crowd workers. Various label aggregation methods were explored to evaluate how to obtain a label that best captures the meaning inferred by the crowd annotators. The results show that a significant proportion of discourse relations in DiscoGeM are ambiguous and can express multiple relation senses. Probability distribution labels better capture these interpretations than single labels. Further, the results emphasize that text genre crucially affects the distribution of discourse relations, suggesting that genre should be included as a factor in automatic relation classification. We make available the newly created DiscoGeM corpus, as well as the dataset with all annotator-level labels. Both the corpus and the dataset can facilitate a multitude of applications and research purposes, for example to function as training data to improve the performance of automatic discourse relation parsers, as well as facilitate research into non-connective signals of discourse relations.},
pubstate = {published},
type = {article}
}

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Abstract: A central issue in linguistics concerns the relationship between theories and evidence in data. We investigate this issue in the field of discourse coherence, and particularly the study of coherence relations such as causal and contrastive. Proposed inventories of coherence relations differ greatly in the type and number of proposed relations. Such proposals are often validated by focusing on either the descriptive adequacy (researcher’s intuitions on textual interpretations) or the cognitive plausibility of distinctions (empirical research on cognition). We argue that both are important, and note that the concept of cognitive plausibility is in need of a concrete definition and quantifiable operationalization. This contribution focuses on how the criterion of cognitive plausibility can be operationalized and presents a systematic validation approach to evaluate discourse frameworks. This is done by detailing how various sources of evidence can be used to support or falsify distinctions between coherence relational labels. Finally, we present methodological issues regarding verification and falsification that are of importance to all discourse researchers studying the relationship between theory and data.

• https://journals.openedition.org/discours/12075

@article{Scholman_etal22,
title = {Descriptively adequate and cognitively plausible? Validating distinctions between types of coherence relations},
author = {Merel Scholman and Vera Demberg and Ted J. M. Sanders},
url = {https://journals.openedition.org/discours/12075},
year = {2022},
date = {2022},
journal = {Discours},
pages = {1-30a},
volume = {30},
abstract = {Abstract: A central issue in linguistics concerns the relationship between theories and evidence in data. We investigate this issue in the field of discourse coherence, and particularly the study of coherence relations such as causal and contrastive. Proposed inventories of coherence relations differ greatly in the type and number of proposed relations. Such proposals are often validated by focusing on either the descriptive adequacy (researcher’s intuitions on textual interpretations) or the cognitive plausibility of distinctions (empirical research on cognition). We argue that both are important, and note that the concept of cognitive plausibility is in need of a concrete definition and quantifiable operationalization. This contribution focuses on how the criterion of cognitive plausibility can be operationalized and presents a systematic validation approach to evaluate discourse frameworks. This is done by detailing how various sources of evidence can be used to support or falsify distinctions between coherence relational labels. Finally, we present methodological issues regarding verification and falsification that are of importance to all discourse researchers studying the relationship between theory and data.},
pubstate = {published},
type = {article}
}

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• https://aclanthology.org/2022.coling-1.322/

@inproceedings{Marchaletal22-2,
title = {Establishing annotation quality in multi-label annotations},
author = {Marian Marchal and Merel Scholman and Frances Pik Yu Yung and Vera Demberg},
url = {https://aclanthology.org/2022.coling-1.322/},
year = {2022},
date = {2022},
booktitle = {Proceedings of the 29th International Conference on Computational Linguistic (COLING)},
pages = {3659–3668},
abstract = {

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Abstract
It is generally assumed that readers draw on their background knowledge to make inferences about information that is left implicit in the text. However, readers may differ in how much background knowledge they have, which may impact their text understanding. The present study investigates the role of domain knowledge in discourse relation interpretation, in order to examine how readers with high vs. low domain knowledge differ in their discourse relation inferences. We compare interpretations of experts from the field of economics and biomedical sciences in scientific biomedical texts as well as more easily accessible economic texts. The results show that high-knowledge readers from the biomedical domain are better at inferring the correct relation interpretation in biomedical texts compared to low-knowledge readers, but such an effect was not found for the economic domain. The results also suggest that, in the absence of domain knowledge, readers exploit linguistic signals other than connectives to infer the discourse relation, but domain knowledge is sometimes required to exploit these cues. The study provides insight into the impact of domain knowledge on discourse relation inferencing and how readers interpret discourse relations when they lack the required domain knowledge.

• https://journals.uic.edu/ojs/index.php/dad/article/view/12343/10711

@article{Marchaletal22,
title = {The effect of domain knowledge on discourse relation inferences: Relation marking and interpretation strategies},
author = {Marian Marchal and Merel Scholman and Vera Demberg},
url = {https://journals.uic.edu/ojs/index.php/dad/article/view/12343/10711},
year = {2022},
date = {2022},
journal = {Dialogue & Discourse},
pages = {49-78},
volume = {13},
number = {(2)},
abstract = {Abstract It is generally assumed that readers draw on their background knowledge to make inferences about information that is left implicit in the text. However, readers may differ in how much background knowledge they have, which may impact their text understanding. The present study investigates the role of domain knowledge in discourse relation interpretation, in order to examine how readers with high vs. low domain knowledge differ in their discourse relation inferences. We compare interpretations of experts from the field of economics and biomedical sciences in scientific biomedical texts as well as more easily accessible economic texts. The results show that high-knowledge readers from the biomedical domain are better at inferring the correct relation interpretation in biomedical texts compared to low-knowledge readers, but such an effect was not found for the economic domain. The results also suggest that, in the absence of domain knowledge, readers exploit linguistic signals other than connectives to infer the discourse relation, but domain knowledge is sometimes required to exploit these cues. The study provides insight into the impact of domain knowledge on discourse relation inferencing and how readers interpret discourse relations when they lack the required domain knowledge.},
pubstate = {published},
type = {article}
}

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• https://deref-web.de/mail/client/HuBZxoWH3kg/dereferrer/?redirectUrl=https%3A%2F%2Fwww.isca-speech.org%2Farchive%2Fspeechprosody_2022%2Fandreeva22_speechprosody.html
• https://doi.org/10.21437/SpeechProsody.2022-155

@inproceedings{andreeva_2022_speechprosody,
title = {The influence of L1 prosody on Bulgarian-accented German and English},
author = {Bistra Andreeva and Snezhina Dimitrova},
url = {https://deref-web.de/mail/client/HuBZxoWH3kg/dereferrer/?redirectUrl=https%3A%2F%2Fwww.isca-speech.org%2Farchive%2Fspeechprosody_2022%2Fandreeva22_speechprosody.html},
doi = {https://doi.org/10.21437/SpeechProsody.2022-155},
year = {2022},
date = {2022},
booktitle = {Proc. Speech Prosody 2022},
pages = {764-768},
address = {Lisbon},
pubstate = {published},
type = {inproceedings}
}

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• https://deref-web.de/mail/client/rap1p9gH2aI/dereferrer/?redirectUrl=https%3A%2F%2Fwww.isca-speech.org%2Farchive%2Fspeechprosody_2022%2Fibrahim22_speechprosody.html
• https://doi.org/10.21437/SpeechProsody.2022-136

@inproceedings{ibrahim_2022_speechprosody,
title = {The effect of predictability on German stop voicing is phonologically selective},
author = {Omnia Ibrahim and Ivan Yuen and Bistra Andreeva and Bernd M{\"o}bius},
url = {https://deref-web.de/mail/client/rap1p9gH2aI/dereferrer/?redirectUrl=https%3A%2F%2Fwww.isca-speech.org%2Farchive%2Fspeechprosody_2022%2Fibrahim22_speechprosody.html},
doi = {https://doi.org/10.21437/SpeechProsody.2022-136},
year = {2022},
date = {2022},
booktitle = {Proc. Speech Prosody 2022},
pages = {669--673},
address = {Lisbon},
pubstate = {forthcoming},
type = {inproceedings}
}

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• https://aclanthology.org/2022.emnlp-main.133

@inproceedings{espana-bonet-barron-cedeno-2022-undesired,
title = {The (Undesired) Attenuation of Human Biases by Multilinguality},
author = {Cristina Espa{\~n}a-Bonet and Alberto Barrón-Cede{\~n}o},
url = {https://aclanthology.org/2022.emnlp-main.133},
year = {2022},
date = {2022},
booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
pages = {2056–2077},
publisher = {Association for Computational Linguistics},
address = {Online and Abu Dhabi, UAE, Dec 2022},
abstract = {

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• https://aclanthology.org/2022.conll-1.9

@inproceedings{bafna-etal-2022-combining,
title = {Combining Noisy Semantic Signals with Orthographic Cues: Cognate Induction for the Indic Dialect Continuum},
author = {Niyati Bafna and Josef van Genabith and Cristina Espa{\~n}a-Bonet and Zdenêk Zabokrtský},
url = {https://aclanthology.org/2022.conll-1.9},
year = {2022},
date = {2022},
booktitle = {Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)},
pages = {110-131},
publisher = {Association for Computational Linguistics},
address = {Abu Dhabi, UAE, Dec 2022},
abstract = {

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Recent work has shown that neural feature- and representation-learning, e.g. BERT, achieves superior performance over traditional manual feature engineering based approaches, with e.g. SVMs, in translationese classification tasks. Previous research did not show (i) whether the difference is because of the features, the classifiers or both, and (ii) what the neural classifiers actually learn. To address (i), we carefully design experiments that swap features between BERT- and SVM-based classifiers. We show that an SVM fed with BERT representations performs at the level of the best BERT classifiers, while BERT learning and using handcrafted features performs at the level of an SVM using handcrafted features. This shows that the performance differences are due to the features. To address (ii) we use integrated gradients and find that (a) there is indication that information captured by hand-crafted features is only a subset of what BERT learns, and (b) part of BERT’s top performance results are due to BERT learning topic differences and spurious correlations with translationese.

• https://aclanthology.org/2022.blackboxnlp-1.23
• https://doi.org/10.48550/ARXIV.2210.13391

@inproceedings{amponsah-kaakyire-etal-2022-explaining,
title = {Explaining Translationese: why are Neural Classifiers Better and what do they Learn?},
author = {Kwabena Amponsah-Kaakyire and Daria Pylypenko and Josef van Genabith and Cristina Espa{\~n}a-Bonet},
url = {https://aclanthology.org/2022.blackboxnlp-1.23},
doi = {https://doi.org/10.48550/ARXIV.2210.13391},
year = {2022},
date = {2022-01-19},
booktitle = {Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP},
pages = {281-296},
publisher = {Association for Computational Linguistics},
address = {Abu Dhabi, United Arab Emirates (Hybrid), Dec 2022},
abstract = {Recent work has shown that neural feature- and representation-learning, e.g. BERT, achieves superior performance over traditional manual feature engineering based approaches, with e.g. SVMs, in translationese classification tasks. Previous research did not show (i) whether the difference is because of the features, the classifiers or both, and (ii) what the neural classifiers actually learn. To address (i), we carefully design experiments that swap features between BERT- and SVM-based classifiers. We show that an SVM fed with BERT representations performs at the level of the best BERT classifiers, while BERT learning and using handcrafted features performs at the level of an SVM using handcrafted features. This shows that the performance differences are due to the features. To address (ii) we use integrated gradients and find that (a) there is indication that information captured by hand-crafted features is only a subset of what BERT learns, and (b) part of BERT's top performance results are due to BERT learning topic differences and spurious correlations with translationese.},
pubstate = {forthcoming},
type = {inproceedings}
}

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Electrophysiological studies suggest that situational event knowledge plays an important role in language processing, but often fail to distinguish whether observed effects are driven by combinatorial expectations, or simple association with the context. In two ERP experiments, participants read short discourses describing ongoing events. We manipulated the situational expectancy of the target word continuing the event as well as the presence of an associated, but inactive event in the context. In both experiments we find an N400 effect for unexpected compared to expected target words, but this effect is significantly attenuated when the unexpected target is nonetheless associated with non-occurring context events. Our findings demonstrate that the N400 is simultaneously influenced by both simple association with – and combinatorial expectations derived from – situational event knowledge. Thus, experimental investigations and comprehension models of the use of event knowledge must accommodate the role of both expectancy and association in electrophysiological measures.

• https://www.tandfonline.com/doi/full/10.1080/23273798.2021.2022171?src=
• https://doi.org/10.1080/23273798.2021.2022171

@article{doi:10.1080/23273798.2021.2022171,
title = {Situational expectancy or association? The influence of event knowledge on the N400},
author = {Elisabeth Rabs and Francesca Delogu and Heiner Drenhaus and Matthew W. Crocker},
url = {https://www.tandfonline.com/doi/full/10.1080/23273798.2021.2022171?src=},
doi = {https://doi.org/10.1080/23273798.2021.2022171},
year = {2022},
date = {2022-01-16},
journal = {Language, Cognition and Neuroscience},
pages = {1-19},
publisher = {Routledge},
abstract = {Electrophysiological studies suggest that situational event knowledge plays an important role in language processing, but often fail to distinguish whether observed effects are driven by combinatorial expectations, or simple association with the context. In two ERP experiments, participants read short discourses describing ongoing events. We manipulated the situational expectancy of the target word continuing the event as well as the presence of an associated, but inactive event in the context. In both experiments we find an N400 effect for unexpected compared to expected target words, but this effect is significantly attenuated when the unexpected target is nonetheless associated with non-occurring context events. Our findings demonstrate that the N400 is simultaneously influenced by both simple association with – and combinatorial expectations derived from – situational event knowledge. Thus, experimental investigations and comprehension models of the use of event knowledge must accommodate the role of both expectancy and association in electrophysiological measures.},
pubstate = {published},
type = {article}
}

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