Publications

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

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

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

The on-line processing of causal and concessive discourse connectives Journal Article

Special Issue on Discourse Expectations, Linguistics, 59, pp. 417-448, 2020.

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{K\"{o}hne2020,
title = {The on-line processing of causal and concessive discourse connectives},
author = {Judith K{\"o}hne and Heiner Drenhaus and Francesca Delogu and Vera Demberg},
url = {https://www.degruyter.com/document/doi/10.1515/ling-2021-0011/html},
doi = {https://doi.org/10.1515/ling-2021-0011},
year = {2020},
date = {2020},
journal = {Special Issue on Discourse Expectations, 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:   C3 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; 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 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; 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 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

Yung, Frances Pik Yu; Demberg, Vera

Do speakers produce discourse connectives rationally? Inproceedings

8th Workshop on Cognitive Aspects of Computational Language Learning and Processing (CogACLL2018), Melbourne, Australia, 2018.

@inproceedings{Yung2019b,
title = {Do speakers produce discourse connectives rationally?},
author = {Frances Pik Yu Yung and Vera Demberg},
year = {2018},
date = {2018},
publisher = {8th Workshop on Cognitive Aspects of Computational Language Learning and Processing (CogACLL2018)},
address = {Melbourne, Australia},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Sanders, Ted J. M.; Demberg, Vera; Hoek, Jet; Torabi Asr, Fatemeh; Zufferey, Sandrine; Evers-Vermeul, Jacqueline

Unifying dimensions in coherence relations: How various annotation frameworks are related Journal Article

Corpus Linguistics and Linguistic Theory, 2018.

In this paper, we show how three often used and seemingly different discourse annotation frameworks – Penn Discourse Treebank (PDTB), Rhetorical Structure Theory (RST), and Segmented Discourse Representation Theory – can be related by using a set of unifying dimensions. These dimensions are taken from the Cognitive approach to Coherence Relations and combined with more fine-grained additional features from the frameworks themselves to yield a posited set of dimensions that can successfully map three frameworks. The resulting interface will allow researchers to find identical or at least closely related relations within sets of annotated corpora, even if they are annotated within different frameworks. Furthermore, we tested our unified dimension (UniDim) approach by comparing PDTB and RST annotations of identical newspaper texts and converting their original end label annotations of relations into the accompanying values per dimension. Subsequently, rates of overlap in the attributed values per dimension were analyzed. Results indicate that the proposed dimensions indeed create an interface that makes existing annotation systems “talk to each other.”

@article{Sanders2018,
title = {Unifying dimensions in coherence relations: How various annotation frameworks are related},
author = {Ted J. M. Sanders and Vera Demberg and Jet Hoek and Fatemeh Torabi Asr and Sandrine Zufferey and Jacqueline Evers-Vermeul},
url = {https://www.degruyter.com/document/doi/10.1515/cllt-2016-0078/html},
doi = {https://doi.org/10.1515/cllt-2016-0078},
year = {2018},
date = {2018-05-22},
journal = {Corpus Linguistics and Linguistic Theory},
abstract = {In this paper, we show how three often used and seemingly different discourse annotation frameworks – Penn Discourse Treebank (PDTB), Rhetorical Structure Theory (RST), and Segmented Discourse Representation Theory – can be related by using a set of unifying dimensions. These dimensions are taken from the Cognitive approach to Coherence Relations and combined with more fine-grained additional features from the frameworks themselves to yield a posited set of dimensions that can successfully map three frameworks. The resulting interface will allow researchers to find identical or at least closely related relations within sets of annotated corpora, even if they are annotated within different frameworks. Furthermore, we tested our unified dimension (UniDim) approach by comparing PDTB and RST annotations of identical newspaper texts and converting their original end label annotations of relations into the accompanying values per dimension. Subsequently, rates of overlap in the attributed values per dimension were analyzed. Results indicate that the proposed dimensions indeed create an interface that makes existing annotation systems “talk to each other.”},
pubstate = {published},
type = {article}
}

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Project:   B2

Hoek, Jet; Scholman, Merel

Evaluating discourse annotation: Some recent insights and new approaches Inproceedings

Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13), 2017.

@inproceedings{hoek2017evaluating,
title = {Evaluating discourse annotation: Some recent insights and new approaches},
author = {Jet Hoek and Merel Scholman},
year = {2017},
date = {2017},
booktitle = {Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13)},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

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

Using explicit discourse connectives in translation for implicit discourse relation classification Inproceedings

8th International Joint Conference on Natural Language Processing, 2017.

@inproceedings{Shi2017b,
title = {Using explicit discourse connectives in translation for implicit discourse relation classification},
author = {Wei Shi and Frances Pik Yu Yung and Raphael Rubino and Vera Demberg},
year = {2017},
date = {2017},
publisher = {8th International Joint Conference on Natural Language Processing},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Shi, Wei; Demberg, Vera

On the need of cross validation for discourse relation classification Inproceedings

European Chapter of the Association for Computational Linguistics (EACL), 2017.

@inproceedings{Shi2017,
title = {On the need of cross validation for discourse relation classification},
author = {Wei Shi and Vera Demberg},
year = {2017},
date = {2017},
publisher = {European Chapter of the Association for Computational Linguistics (EACL)},
pubstate = {published},
type = {inproceedings}
}

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Project:   B2

Rohde, Hannah; Demberg, Vera

"On the one hand" as a cue to anticipate upcoming discourse structure Journal Article

Journal of Memory and Language, 97, pp. 47-60, 2017.

@article{Merel2017,
title = {"On the one hand" as a cue to anticipate upcoming discourse structure},
author = {Hannah Rohde and Vera Demberg},
year = {2017},
date = {2017},
journal = {Journal of Memory and Language},
pages = {47-60},
volume = {97},
pubstate = {published},
type = {article}
}

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Project:   B2

Demberg, Vera

Examples and specifications that prove a point: Distinguishing between elaborative and argumentative discourse relations Journal Article

Dialogue and Discourse, 8, pp. 53-86, 2017.

@article{Scholman2017,
title = {Examples and specifications that prove a point: Distinguishing between elaborative and argumentative discourse relations},
author = {Vera Demberg},
year = {2017},
date = {2017},
journal = {Dialogue and Discourse},
pages = {53-86},
volume = {8},
number = {2},
pubstate = {published},
type = {article}
}

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Project:   B2

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