Publications

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.

• abdullah22_interspeech     (5.75MB)
• 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 = {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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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.

• gessinger22_interspeech     (0.23MB)
• 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 = {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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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.

• pardo_etal_jphon-si2022     (0.36MB)
• https://www.coli.uni-saarland.de/~moebius/documents/pardo_etal_jphon-si2022.pdf

@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.coli.uni-saarland.de/~moebius/documents/pardo_etal_jphon-si2022.pdf},
year = {2022},
date = {2022},
journal = {Journal of Phonetics},
pages = {paper 101196},
volume = {95, 1-9},
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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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 = {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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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 = {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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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 = {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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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 = {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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In many linguistic fields requiring annotated data, multiple interpretations of a single item are possible. Multi-label annotations more accurately reflect this possibility. However, allowing for multi-label annotations also affects the chance that two coders agree with each other. Calculating inter-coder agreement for multi-label datasets is therefore not trivial. In the current contribution, we evaluate different metrics for calculating agreement on multi-label annotations: agreement on the intersection of annotated labels, an augmented version of Cohen’s Kappa, and precision, recall and F1. We propose a bootstrapping method to obtain chance agreement for each measure, which allows us to obtain an adjusted agreement coefficient that is more interpretable. We demonstrate how various measures affect estimates of agreement on simulated datasets and present a case study of discourse relation annotations. We also show how the proportion of double labels, and the entropy of the label distribution, influences the measures outlined above and how a bootstrapped adjusted agreement can make agreement measures more comparable across datasets in multi-label scenarios.

• 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 = {In many linguistic fields requiring annotated data, multiple interpretations of a single item are possible. Multi-label annotations more accurately reflect this possibility. However, allowing for multi-label annotations also affects the chance that two coders agree with each other. Calculating inter-coder agreement for multi-label datasets is therefore not trivial. In the current contribution, we evaluate different metrics for calculating agreement on multi-label annotations: agreement on the intersection of annotated labels, an augmented version of Cohen’s Kappa, and precision, recall and F1. We propose a bootstrapping method to obtain chance agreement for each measure, which allows us to obtain an adjusted agreement coefficient that is more interpretable. We demonstrate how various measures affect estimates of agreement on simulated datasets and present a case study of discourse relation annotations. We also show how the proportion of double labels, and the entropy of the label distribution, influences the measures outlined above and how a bootstrapped adjusted agreement can make agreement measures more comparable across datasets in multi-label scenarios.},
pubstate = {published},
type = {inproceedings}
}

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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.

• DD_MarchalScholmanDemberg_2109     (0.25MB)
• 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 = {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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The present study investigates L2 prosodic realizations in the readings of two groups of Bulgarian informants: (a) with L2 German, and (b) with L2 English. Each group consisted of ten female learners, who read the fable “The North Wind and the Sun” in their L1 and in the respective L2. We also recorded two groups of female native speakers of the target languages as controls. The following durational parameters were obtained: mean accented syllable duration, accented/naccented duration ratio, speaking rate. With respect to F0 parameters, mean, median, minimum, maximum, span in semitones, and standard deviations per IP were measured. Additionally, we calculated the number of accented and unaccented syllables, IPs and pauses in each reading. Statistical analyses show that the two groups differ in their use of F0. Both groups use higher standard deviation and level in their L2, whereas the ‘German group’ use higher pitch span as well. The number of accented syllables, IPs and pauses is also higher in L2. Regarding duration, both groups use slower articulation rate. The accented/unaccented syllable duration ratio is lower in L2 for the ‘English group’. We also provide original data on speaking rate in Bulgarian from an information theoretical perspective.

• https://www.isca-speech.org/archive/speechprosody_2022/andreeva22_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://www.isca-speech.org/archive/speechprosody_2022/andreeva22_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},
abstract = {The present study investigates L2 prosodic realizations in the readings of two groups of Bulgarian informants: (a) with L2 German, and (b) with L2 English. Each group consisted of ten female learners, who read the fable “The North Wind and the Sun” in their L1 and in the respective L2. We also recorded two groups of female native speakers of the target languages as controls. The following durational parameters were obtained: mean accented syllable duration, accented/naccented duration ratio, speaking rate. With respect to F0 parameters, mean, median, minimum, maximum, span in semitones, and standard deviations per IP were measured. Additionally, we calculated the number of accented and unaccented syllables, IPs and pauses in each reading. Statistical analyses show that the two groups differ in their use of F0. Both groups use higher standard deviation and level in their L2, whereas the ‘German group’ use higher pitch span as well. The number of accented syllables, IPs and pauses is also higher in L2. Regarding duration, both groups use slower articulation rate. The accented/unaccented syllable duration ratio is lower in L2 for the ‘English group’. We also provide original data on speaking rate in Bulgarian from an information theoretical perspective.},
pubstate = {published},
type = {inproceedings}
}

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Cross-linguistic evidence suggests that syllables in predictable contexts have shorter duration than in unpredictable contexts. However, it is not clear if predictability uniformly affects phonetic cues of a phonological feature in a segment. The current study explored the effect of syllable-based predictability on the durational correlates of the phonological stop voicing contrast in German, viz. voice onset time (VOT) and closure duration (CD), using data in Ibrahim et al. [1]. The target stop consonants /b, p, d, k/ occurred in stressed CV syllables in polysyllabic words embedded in a sentence, with either voiced or voiceless preceding contexts. The syllable occurred in either a low or a high predictable condition, which was based on a syllable-level trigram language model. We measured VOT and CD of the target consonants (voiced vs. voiceless). Our results showed an interaction effect of predictability and the voicing status of the target consonants on VOT, but a uniform effect on closure duration. This interaction effect on a primary cue like VOT indicates a selective effect of predictability on VOT, but not on CD. This suggests that the effect of predictability is sensitive to the phonological relevance of a language-specific phonetic cue.

• ibrahim22_speechprosody     (3.62MB)
• https://www.isca-speech.org/archive/pdfs/speechprosody_2022/ibrahim22_speechprosody.pdf
• 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://www.isca-speech.org/archive/pdfs/speechprosody_2022/ibrahim22_speechprosody.pdf},
doi = {https://doi.org/10.21437/SpeechProsody.2022-136},
year = {2022},
date = {2022},
booktitle = {Proc. Speech Prosody 2022},
pages = {669-673},
address = {Lisbon},
abstract = {Cross-linguistic evidence suggests that syllables in predictable contexts have shorter duration than in unpredictable contexts. However, it is not clear if predictability uniformly affects phonetic cues of a phonological feature in a segment. The current study explored the effect of syllable-based predictability on the durational correlates of the phonological stop voicing contrast in German, viz. voice onset time (VOT) and closure duration (CD), using data in Ibrahim et al. [1]. The target stop consonants /b, p, d, k/ occurred in stressed CV syllables in polysyllabic words embedded in a sentence, with either voiced or voiceless preceding contexts. The syllable occurred in either a low or a high predictable condition, which was based on a syllable-level trigram language model. We measured VOT and CD of the target consonants (voiced vs. voiceless). Our results showed an interaction effect of predictability and the voicing status of the target consonants on VOT, but a uniform effect on closure duration. This interaction effect on a primary cue like VOT indicates a selective effect of predictability on VOT, but not on CD. This suggests that the effect of predictability is sensitive to the phonological relevance of a language-specific phonetic cue.},
pubstate = {published},
type = {inproceedings}
}

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• 8Talamo     (0.53MB)
• https://www.italian-journal-linguistics.com/app/uploads/2023/01/8-Talamo.pdf
• https://doi.org/10.26346/1120-2726-197

@article{article,
title = {A new methodology for an old problem: A corpus-based typology of adnominal word order in European languages},
author = {Luigi Talamo and Annemarie Verkerk},
url = {https://www.italian-journal-linguistics.com/app/uploads/2023/01/8-Talamo.pdf},
doi = {https://doi.org/10.26346/1120-2726-197},
year = {2022},
date = {2022},
journal = {Italian Journal of Linguistics},
pages = {171-226},
volume = {34},
abstract = {

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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 = {published},
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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Closely related languages are often mutually intelligible to various degrees. Therefore, speakers of closely related languages are usually capable of (partially) comprehending each other’s speech without explicitly learning the target, second language. The cross-linguistic intelligibility among closely related languages is mainly driven by linguistic factors such as lexical similarities. This paper presents a computational model of spoken-word recognition and investigates its ability to recognize word forms from different languages than its native, training language. Our model is based on a recurrent neural network that learns to map a word’s phonological sequence onto a semantic representation of the word. Furthermore, we present a case study on the related Slavic languages and demonstrate that the cross-lingual performance of our model not only predicts mutual intelligibility to a large extent but also reflects the genetic classification of the languages in our study.

• https://aclanthology.org/2022.vardial-1.6/

@inproceedings{zaitova-etal-2022-mapping,
title = {Mapping Phonology to Semantics: A Computational Model of Cross-Lingual Spoken-Word Recognition},
author = {Iuliia Zaitova and Badr M. Abdullah and Dietrich Klakow},
url = {https://aclanthology.org/2022.vardial-1.6/},
year = {2022},
date = {2022},
booktitle = {Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects (October 2022, Gyeongju, Republic of Korea)},
pages = {54-63},
publisher = {Association for Computational Linguistics},
abstract = {Closely related languages are often mutually intelligible to various degrees. Therefore, speakers of closely related languages are usually capable of (partially) comprehending each other’s speech without explicitly learning the target, second language. The cross-linguistic intelligibility among closely related languages is mainly driven by linguistic factors such as lexical similarities. This paper presents a computational model of spoken-word recognition and investigates its ability to recognize word forms from different languages than its native, training language. Our model is based on a recurrent neural network that learns to map a word’s phonological sequence onto a semantic representation of the word. Furthermore, we present a case study on the related Slavic languages and demonstrate that the cross-lingual performance of our model not only predicts mutual intelligibility to a large extent but also reflects the genetic classification of the languages in our study.},
pubstate = {published},
type = {inproceedings}
}

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Implicit discourse relations can convey more than one relation sense, but much of the research on discourse relations has focused on single relation senses. Recently, DiscoGeM, a novel multi-domain corpus, which contains 10 crowd-sourced labels per relational instance, has become available. In this paper, we analyse the co-occurrences of relations in DiscoGem and show that they are systematic and characteristic of text genre. We then test whether information on multi-label distributions in the data can help implicit relation classifiers. Our results show that incorporating multiple labels in parser training can improve its performance, and yield label distributions which are more similar to human label distributions, compared to a parser that is trained on just a single most frequent label per instance.

• https://aclanthology.org/2022.codi-1.7

@inproceedings{Yungetal2022,
title = {Label distributions help implicit discourse relation classification},
author = {Frances Pik Yu Yung and Kaveri Anuranjana and Merel Scholman and Vera Demberg},
url = {https://aclanthology.org/2022.codi-1.7},
year = {2022},
date = {2022},
booktitle = {Proceedings of the 3rd Workshop on Computational Approaches to Discourse (October 2022, Gyeongju, Republic of Korea and Online)},
pages = {48–53},
publisher = {International Conference on Computational Linguistics},
abstract = {Implicit discourse relations can convey more than one relation sense, but much of the research on discourse relations has focused on single relation senses. Recently, DiscoGeM, a novel multi-domain corpus, which contains 10 crowd-sourced labels per relational instance, has become available. In this paper, we analyse the co-occurrences of relations in DiscoGem and show that they are systematic and characteristic of text genre. We then test whether information on multi-label distributions in the data can help implicit relation classifiers. Our results show that incorporating multiple labels in parser training can improve its performance, and yield label distributions which are more similar to human label distributions, compared to a parser that is trained on just a single most frequent label per instance.},
pubstate = {published},
type = {inproceedings}
}

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Semantic extension plays a key role in language change and grammaticalisation. Here we use a dyadic interaction paradigm to study semantic extension of novel labels in controlled circumstances. We ask whether participants will be able to (i) use highly accessible associations in the perceptual environment (colour-shape associations) to converge on a meaning for the novel labels, and (ii) extend these meanings to apply to both concrete targets (objects) and abstract targets (emotions). Further, given the argument that both metonymy and metaphor are important drivers of language change, we investigate whether participants will be able to draw on relations of contiguity (‘metonymic’ associations, e.g. colour-shape or object-colour) and relations of similarity (‘metaphorical’ associations, e.g. emotion-colour) to extend the meaning of labels.

• eScholarship UC item 7w22b8gm     (0.34MB)
• https://escholarship.org/uc/item/7w22b8gm

@inproceedings{HaeuserKray2022,
title = {Uninvited and unwanted: False memories for words predicted but not seen},
author = {Katja H{\"a}user and Jutta Kray},
editor = {Jennifer Culbertson and Andrew Perfors and Hugh Rabagliati and Veronica Ramenzoni},
url = {https://escholarship.org/uc/item/7w22b8gm},
year = {2022},
date = {2022},
booktitle = {Proceedings of the 44th Annual Conference of the Cognitive Science Society, Toronto, Canada (27 Jul 2022 - 30 Jul 2022)},
pages = {2401-2408},
abstract = {Semantic extension plays a key role in language change and grammaticalisation. Here we use a dyadic interaction paradigm to study semantic extension of novel labels in controlled circumstances. We ask whether participants will be able to (i) use highly accessible associations in the perceptual environment (colour-shape associations) to converge on a meaning for the novel labels, and (ii) extend these meanings to apply to both concrete targets (objects) and abstract targets (emotions). Further, given the argument that both metonymy and metaphor are important drivers of language change, we investigate whether participants will be able to draw on relations of contiguity (‘metonymic’ associations, e.g. colour-shape or object-colour) and relations of similarity (‘metaphorical’ associations, e.g. emotion-colour) to extend the meaning of labels.},
pubstate = {published},
type = {inproceedings}
}

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• collabra_2022_8_1_36945     (1.55MB)
• https://online.ucpress.edu/collabra/article/8/1/36945/187814/Hedging-Bets-in-Linguistic-Prediction-Younger-and
• https://doi.org/10.1525/collabra.36945

@article{Haeuseretal22,
title = {Hedging Bets in Linguistic Prediction: Younger and Older Adults Vary in the Breadth of Predictive Processing},
author = {Katja H{\"a}user and Jutta Kray and Arielle Borovsky},
url = {https://online.ucpress.edu/collabra/article/8/1/36945/187814/Hedging-Bets-in-Linguistic-Prediction-Younger-and},
doi = {https://doi.org/10.1525/collabra.36945},
year = {2022},
date = {2022},
journal = {Collabra: Psychology},
volume = {8(1):36945},
abstract = {

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