Publications

Shi, Wei; Demberg, Vera

Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain Inproceedings

Proceedings of the Joint Conference of the 59th Annual Meeting of theAssociation for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), 2021.

Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.

@inproceedings{shi2021entity,
title = {Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain},
author = {Wei Shi and Vera Demberg},
url = {https://aclanthology.org/2021.acl-short.116.pdf},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Joint Conference of the 59th Annual Meeting of theAssociation for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)},
abstract = {Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Marchal, Marian; Scholman, Merel; Demberg, Vera

Semi-automatic discourse annotation in a low-resource language: Developing a connective lexicon for Nigerian Pidgin Inproceedings

Proceedings of the Second Workshop on Computational Approaches to Discourse (CODI 2021), 2021.

@inproceedings{marchal2021,
title = {Semi-automatic discourse annotation in a low-resource language: Developing a connective lexicon for Nigerian Pidgin},
author = {Marian Marchal and Merel Scholman and Vera Demberg},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Second Workshop on Computational Approaches to Discourse (CODI 2021)},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Demberg, Vera; Torabi Asr, Fatemeh; Scholman, Merel

DiscAlign for Penn and RST Discourse Treebanks Miscellaneous

Linguistic Data Consortium, Philadelphia, 2021, ISBN 1-58563-975-3.

@miscellaneous{Demberg_etal_DiscAlign,
title = {DiscAlign for Penn and RST Discourse Treebanks},
author = {Vera Demberg and Fatemeh Torabi Asr and Merel Scholman},
doi = {https://doi.org/10.35111/cf0q-c454},
year = {2021},
date = {2021-09-15},
isbn = {1-58563-975-3},
publisher = {Linguistic Data Consortium},
address = {Philadelphia},
pubstate = {published},
type = {miscellaneous}
}

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Project:   B2

Crible, Ludivine; Demberg, Vera

The role of non-connective discourse cues and their interaction with connectives Journal Article Forthcoming

Pragmatics and Cognition, 2021.

The disambiguation and processing of coherence relations is often investigated with a focus on explicit connectives, such as but or so. Other, non-connective cues from the context also facilitate discourse inferences, although their precise disambiguating role and interaction with connectives have been largely overlooked in the psycholinguistic literature so far. This study reports on two crowdsourcing experiments that test the role of contextual cues (parallelism, antonyms, resultative verbs) in the disambiguation of contrast and consequence relations. We compare the effect of contextual cues in conceptually different relations, and with connectives that differ in their semantic precision. Using offline tasks, our results show that contextual cues significantly help disambiguating contrast and consequence relations in the absence of connectives. However, when connectives are present in the context, the effect of cues only holds if the connective is acceptable in the target relation. Overall, our study suggests that cues are decisive on their own, but only secondary in the presence of connectives. These results call for further investigation of the complex interplay between connective types, contextual cues, relation types and other linguistic and cognitive factors.

@article{Crible2021,
title = {The role of non-connective discourse cues and their interaction with connectives},
author = {Ludivine Crible and Vera Demberg},
year = {2021},
date = {2021},
journal = {Pragmatics and Cognition},
abstract = {The disambiguation and processing of coherence relations is often investigated with a focus on explicit connectives, such as but or so. Other, non-connective cues from the context also facilitate discourse inferences, although their precise disambiguating role and interaction with connectives have been largely overlooked in the psycholinguistic literature so far. This study reports on two crowdsourcing experiments that test the role of contextual cues (parallelism, antonyms, resultative verbs) in the disambiguation of contrast and consequence relations. We compare the effect of contextual cues in conceptually different relations, and with connectives that differ in their semantic precision. Using offline tasks, our results show that contextual cues significantly help disambiguating contrast and consequence relations in the absence of connectives. However, when connectives are present in the context, the effect of cues only holds if the connective is acceptable in the target relation. Overall, our study suggests that cues are decisive on their own, but only secondary in the presence of connectives. These results call for further investigation of the complex interplay between connective types, contextual cues, relation types and other linguistic and cognitive factors.},
pubstate = {forthcoming},
type = {article}
}

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Project:   B2

Yung, Frances Pik Yu; Jungbluth, Jana; Demberg, Vera

Limits to the Rational Production of Discourse Connectives Journal Article

Frontiers in Psychology, 12, pp. 1729, 2021.

Rational accounts of language use such as the uniform information density hypothesis, which asserts that speakers distribute information uniformly across their utterances, and the rational speech act (RSA) model, which suggests that speakers optimize the formulation of their message by reasoning about what the comprehender would understand, have been hypothesized to account for a wide range of language use phenomena. We here specifically focus on the production of discourse connectives. While there is some prior work indicating that discourse connective production may be governed by RSA, that work uses a strongly gamified experimental setting. In this study, we aim to explore whether speakers reason about the interpretation of their conversational partner also in more realistic settings. We thereby systematically vary the task setup to tease apart effects of task instructions and effects of the speaker explicitly seeing the interpretation alternatives for the listener. Our results show that the RSA-predicted effect of connective choice based on reasoning about the listener is only found in the original setting where explicit interpretation alternatives of the listener are available for the speaker. The effect disappears when the speaker has to reason about listener interpretations. We furthermore find that rational effects are amplified by the gamified task setting, indicating that meta-reasoning about the specific task may play an important role and potentially limit the generalizability of the found effects to more naturalistic every-day language use.

@article{yungJungbluthDemberg2021,
title = {Limits to the Rational Production of Discourse Connectives},
author = {Frances Pik Yu Yung and Jana Jungbluth and Vera Demberg},
url = {https://www.frontiersin.org/article/10.3389/fpsyg.2021.660730},
doi = {https://doi.org/10.3389/fpsyg.2021.660730},
year = {2021},
date = {2021-05-28},
journal = {Frontiers in Psychology},
pages = {1729},
volume = {12},
abstract = {Rational accounts of language use such as the uniform information density hypothesis, which asserts that speakers distribute information uniformly across their utterances, and the rational speech act (RSA) model, which suggests that speakers optimize the formulation of their message by reasoning about what the comprehender would understand, have been hypothesized to account for a wide range of language use phenomena. We here specifically focus on the production of discourse connectives. While there is some prior work indicating that discourse connective production may be governed by RSA, that work uses a strongly gamified experimental setting. In this study, we aim to explore whether speakers reason about the interpretation of their conversational partner also in more realistic settings. We thereby systematically vary the task setup to tease apart effects of task instructions and effects of the speaker explicitly seeing the interpretation alternatives for the listener. Our results show that the RSA-predicted effect of connective choice based on reasoning about the listener is only found in the original setting where explicit interpretation alternatives of the listener are available for the speaker. The effect disappears when the speaker has to reason about listener interpretations. We furthermore find that rational effects are amplified by the gamified task setting, indicating that meta-reasoning about the specific task may play an important role and potentially limit the generalizability of the found effects to more naturalistic every-day language use.},
pubstate = {published},
type = {article}
}

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Project:   B2

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

The online processing of causal and concessive discourse connectives Journal Article

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

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

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

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Projects:   A1 B2 B3

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

Is there less agreement when the discourse is underspecified? Inproceedings

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

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

@inproceedings{hoek-etal-2021-discann,
title = {Is there less agreement when the discourse is underspecified?},
author = {Jet Hoek and Merel Scholman and Ted J. M. Sanders},
url = {https://discannworkshop.github.io/papers/DiscAnn_2020_HoekScholmanSanders_10Sept2021.pdf},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Integrating Perspectives on Discourse Annotation (DiscAnn) Workshop},
address = {University of T{\"u}bingen, Germany},
abstract = {When annotating coherence relations, interannotator agreement tends to be lower on implicit relations than on relations that are explicitly marked by means of a connective or a cue phrase. This paper explores one possible explanation for this: the additional inferencing involved in interpreting implicit relations compared to explicit relations. If this is the main source of disagreements, agreement should be highly related to the specificity of the connective. Using the CCR framework, we annotated relations from TED talks that were marked by a very specific marker, marked by a highly ambiguous connective, or not marked by means of a connective at all. We indeed reached higher inter-annotator agreement on explicit than on implicit relations. However, agreement on underspecified relations was not necessarily in between, which is what would be expected if agreement on implicit relations mainly suffers because annotators have less specific instructions for inferring the relation.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

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

A practical perspective on connective generation Inproceedings

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

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

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

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Project:   B2

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

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

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

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

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

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Project:   B2

Scholman, Merel; Demberg, Vera; Sanders, Ted J. M.

Individual differences in expecting coherence relations: Exploring the variability in sensitivity to contextual signals in discourse Journal Article

Discourse Processes, 57, pp. 844-861, 2020.

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.

@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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Project:   B2

Crible, Ludivine; Demberg, Vera

When Do We Leave Discourse Relations Underspecified? The Effect of Formality and Relation Type Journal Article

Discours, 2020.

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.

@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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Project:   B2

Torabi Asr, Fatemeh; Demberg, Vera

Interpretation of Discourse Connectives Is Probabilistic: Evidence From the Study of But and Although Journal Article

Discourse Processes, 57, pp. 376-399, 2020.

Connectives can facilitate the processing of discourse relations by helping comprehenders to infer the intended coherence relation holding between two text spans. Previous experimental studies have focused on pairs of connectives that are very different from one another to be able to compare and formalize the distinguishing effects of these particles in discourse comprehension. In this article, we compare two connectives, but and although, which overlap in terms of the relations they can signal. We demonstrate in a set of carefully controlled studies that while a connective can be a marker of several discourse relations, it can have a specific fine-grained biasing effect on linguistic inferences and that this bias can be derived (or predicted) from the connectives’ distribution of relations found in production data. The effects that we find speak to the ambiguity of discourse connectives, in general, and the different functions of but and although, in particular. These effects cannot be explained within the earlier accounts of discourse connectives, which propose that each connective has a core meaning or processing instruction. Instead, we here lay out a probabilistic account of connective meaning and interpretation, which is based on the distribution of connectives in production and is supported by our experimental findings.

@article{torabi2020interpretation,
title = {Interpretation of Discourse Connectives Is Probabilistic: Evidence From the Study of But and Although},
author = {Fatemeh Torabi Asr and Vera Demberg},
url = {https://www.tandfonline.com/doi/full/10.1080/0163853X.2019.1700760},
doi = {https://doi.org/10.1080/0163853X.2019.1700760},
year = {2020},
date = {2020-01-27},
journal = {Discourse Processes},
pages = {376-399},
volume = {57},
number = {4},
abstract = {Connectives can facilitate the processing of discourse relations by helping comprehenders to infer the intended coherence relation holding between two text spans. Previous experimental studies have focused on pairs of connectives that are very different from one another to be able to compare and formalize the distinguishing effects of these particles in discourse comprehension. In this article, we compare two connectives, but and although, which overlap in terms of the relations they can signal. We demonstrate in a set of carefully controlled studies that while a connective can be a marker of several discourse relations, it can have a specific fine-grained biasing effect on linguistic inferences and that this bias can be derived (or predicted) from the connectives’ distribution of relations found in production data. The effects that we find speak to the ambiguity of discourse connectives, in general, and the different functions of but and although, in particular. These effects cannot be explained within the earlier accounts of discourse connectives, which propose that each connective has a core meaning or processing instruction. Instead, we here lay out a probabilistic account of connective meaning and interpretation, which is based on the distribution of connectives in production and is supported by our experimental findings.},
pubstate = {published},
type = {article}
}

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Project:   B2

Shi, Wei; Demberg, Vera

Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains Inproceedings

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, pp. 5789-5795, Hong Kong, China, 2019.

Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. Previous studies have generally focused on extracting better representations of the relational arguments. In order to solve the task, it is however additionally necessary to capture what events are expected to cause or follow each other. Current discourse relation classifiers fall short in this respect. We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. (2019), which were trained on a nextsentence prediction task, and thus encode a representation of likely next sentences. The BERT-based model outperforms the current state of the art in 11-way classification by 8% points on the standard PDTB dataset. Our experiments also demonstrate that the model can be successfully ported to other domains: on the BioDRB dataset, the model outperforms
the state of the art system around 15% points.

@inproceedings{shi-demberg-2019-next,
title = {Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains},
author = {Wei Shi and Vera Demberg},
url = {https://www.aclweb.org/anthology/D19-1586},
doi = {https://doi.org/10.18653/v1/D19-1586},
year = {2019},
date = {2019-11-03},
booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages = {5789-5795},
publisher = {Association for Computational Linguistics},
address = {Hong Kong, China},
abstract = {Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. Previous studies have generally focused on extracting better representations of the relational arguments. In order to solve the task, it is however additionally necessary to capture what events are expected to cause or follow each other. Current discourse relation classifiers fall short in this respect. We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. (2019), which were trained on a nextsentence prediction task, and thus encode a representation of likely next sentences. The BERT-based model outperforms the current state of the art in 11-way classification by 8% points on the standard PDTB dataset. Our experiments also demonstrate that the model can be successfully ported to other domains: on the BioDRB dataset, the model outperforms the state of the art system around 15% points.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Scholman, Merel

Coherence relations in discourse and cognition: comparing approaches, annotations, and interpretations PhD Thesis

Saarland University, Saarbruecken, Germany, 2019.

When readers comprehend a discourse, they do not merely interpret each clause or sentence separately; rather, they assign meaning to the text by creating semantic links between the clauses and sentences. These links are known as coherence relations (cf. Hobbs, 1979; Sanders, Spooren & Noordman, 1992).

If readers are not able to construct such relations between the clauses and sentences of a text, they will fail to fully understand that text. Discourse coherence is therefore crucial to natural language comprehension in general. Most frameworks that propose inventories of coherence relation types agree on the existence of certain coarse-grained relation types, such as causal relations (relations types belonging to the causal class include Cause or Result relations), and additive relations (e.g., Conjunctions or Specifications). However, researchers often disagree on which finer-grained relation types hold and, as a result, there is no uniform set of relations that the community has agreed on (Hovy & Maier, 1995). Using a combination of corpus-based studies and off-line and on-line experimental methods, the studies reported in this dissertation examine distinctions between types of relations.

The studies are based on the argument that coherence relations are cognitive entities, and distinctions of coherence relation types should therefore be validated using observations that speak to both the descriptive adequacy and the cognitive plausibility of the distinctions. Various distinctions between relation types are investigated on several levels, corresponding to the central challenges of the thesis. First, the distinctions that are made in approaches to coherence relations are analysed by comparing the relational classes and assessing the theoretical correspondences between the proposals. An interlingua is developed that can be used to map relational labels from one approach to another, therefore improving the interoperability between the different approaches. Second, practical correspondences between different approaches are studied by evaluating datasets containing coherence relation annotations from multiple approaches. A comparison of the annotations from different approaches on the same data corroborate the interlingua, but also reveal systematic patterns of discrepancies between the frameworks that are caused by different operationalizations.

Finally, in the experimental part of the dissertation, readers’ interpretations are investigated to determine whether readers are able to distinguish between specific types of relations that cause the discrepancies between approaches. Results from off-line and online studies provide insight into readers’ interpretations of multi-interpretable relations, individual differences in interpretations, anticipation of discourse structure, and distributional differences between languages on readers’ processing of discourse. In sum, the studies reported in this dissertation contribute to a more detailed understanding of which types of relations comprehenders construct and how these relations are inferred and processed.

@phdthesis{Scholman_diss_2019,
title = {Coherence relations in discourse and cognition: comparing approaches, annotations, and interpretations},
author = {Merel Scholman},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:291--ds-278687},
doi = {https://doi.org/http://dx.doi.org/10.22028/D291-27868},
year = {2019},
date = {2019},
school = {Saarland University},
address = {Saarbruecken, Germany},
abstract = {When readers comprehend a discourse, they do not merely interpret each clause or sentence separately; rather, they assign meaning to the text by creating semantic links between the clauses and sentences. These links are known as coherence relations (cf. Hobbs, 1979; Sanders, Spooren & Noordman, 1992). If readers are not able to construct such relations between the clauses and sentences of a text, they will fail to fully understand that text. Discourse coherence is therefore crucial to natural language comprehension in general. Most frameworks that propose inventories of coherence relation types agree on the existence of certain coarse-grained relation types, such as causal relations (relations types belonging to the causal class include Cause or Result relations), and additive relations (e.g., Conjunctions or Specifications). However, researchers often disagree on which finer-grained relation types hold and, as a result, there is no uniform set of relations that the community has agreed on (Hovy & Maier, 1995). Using a combination of corpus-based studies and off-line and on-line experimental methods, the studies reported in this dissertation examine distinctions between types of relations. The studies are based on the argument that coherence relations are cognitive entities, and distinctions of coherence relation types should therefore be validated using observations that speak to both the descriptive adequacy and the cognitive plausibility of the distinctions. Various distinctions between relation types are investigated on several levels, corresponding to the central challenges of the thesis. First, the distinctions that are made in approaches to coherence relations are analysed by comparing the relational classes and assessing the theoretical correspondences between the proposals. An interlingua is developed that can be used to map relational labels from one approach to another, therefore improving the interoperability between the different approaches. Second, practical correspondences between different approaches are studied by evaluating datasets containing coherence relation annotations from multiple approaches. A comparison of the annotations from different approaches on the same data corroborate the interlingua, but also reveal systematic patterns of discrepancies between the frameworks that are caused by different operationalizations. Finally, in the experimental part of the dissertation, readers’ interpretations are investigated to determine whether readers are able to distinguish between specific types of relations that cause the discrepancies between approaches. Results from off-line and online studies provide insight into readers’ interpretations of multi-interpretable relations, individual differences in interpretations, anticipation of discourse structure, and distributional differences between languages on readers’ processing of discourse. In sum, the studies reported in this dissertation contribute to a more detailed understanding of which types of relations comprehenders construct and how these relations are inferred and processed.},
pubstate = {published},
type = {phdthesis}
}

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Project:   B2

Zhai, Fangzhou; Demberg, Vera; Shkadzko, Pavel; Shi, Wei; Sayeed, Asad

A Hybrid Model for Globally Coherent Story Generation Inproceedings

Proceedings of the Second Workshop on Storytelling, Association for Computational Linguistics, pp. 34-45, Florence, IT, 2019.

@inproceedings{Fangzhou2019,
title = {A Hybrid Model for Globally Coherent Story Generation},
author = {Fangzhou Zhai and Vera Demberg and Pavel Shkadzko and Wei Shi and Asad Sayeed},
year = {2019},
date = {2019},
booktitle = {Proceedings of the Second Workshop on Storytelling},
pages = {34-45},
publisher = {Association for Computational Linguistics},
address = {Florence, IT},
pubstate = {published},
type = {inproceedings}
}

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Projects:   A3 B2

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

Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task Inproceedings

Linguistic Annotation Workshop at ACL. LAW XIII 2019, 2019.

@inproceedings{Yung2019,
title = {Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task},
author = {Frances Pik Yu Yung and Merel Scholman and Vera Demberg},
year = {2019},
date = {2019-08-01},
publisher = {Linguistic Annotation Workshop at ACL. LAW XIII 2019},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Shi, Wei; Yung, Frances Pik Yu; Demberg, Vera

Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification Inproceedings

In Proceedings of Discourse Relation Parsing and Treebanking (DISRPT@NAACL-2019), pp. 12-21, Minneapolis, USA, 2019.

@inproceedings{Shi2019,
title = {Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification},
author = {Wei Shi and Frances Pik Yu Yung and Vera Demberg},
year = {2019},
date = {2019-06-06},
booktitle = {In Proceedings of Discourse Relation Parsing and Treebanking (DISRPT@NAACL-2019)},
pages = {12-21},
address = {Minneapolis, USA},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Demberg, Vera; Scholman, Merel; Torabi Asr, Fatemeh

How compatible are our discourse annotation frameworks? Insights from mapping RST-DT and PDTB annotations Journal Article

Dialogue & Discourse , 10, pp. 87-135, 2019.

@article{Demberg2019,
title = {How compatible are our discourse annotation frameworks? Insights from mapping RST-DT and PDTB annotations},
author = {Vera Demberg and Merel Scholman and Fatemeh Torabi Asr},
year = {2019},
date = {2019-06-01},
journal = {Dialogue & Discourse},
pages = {87-135},
volume = {10},
number = {1},
pubstate = {published},
type = {article}
}

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Project:   B2

Shi, Wei; Demberg, Vera

Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification Inproceedings

In Proceedings of the 13th International Conference on Computational Semantics (IWCS-2019), pp. 188-199, Gothenburg, 2019.

@inproceedings{Shi2019b,
title = {Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification},
author = {Wei Shi and Vera Demberg},
year = {2019},
date = {2019-05-23},
booktitle = {In Proceedings of the 13th International Conference on Computational Semantics (IWCS-2019)},
pages = {188-199},
address = {Gothenburg},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Crible, Ludivine; Demberg, Vera

The effect of genre variation on the production and acceptability of underspecified discourse markers in English Inproceedings

20th DiscourseNet, 2018.

@inproceedings{Crible2018,
title = {The effect of genre variation on the production and acceptability of underspecified discourse markers in English},
author = {Ludivine Crible and Vera Demberg},
year = {2018},
date = {2018},
publisher = {20th DiscourseNet},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

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