Publications

In discourse relation recognition, the classification labels are typically represented as one-hot vectors. However, the categories are in fact not all independent of one another on the contrary, there are several frameworks that describe the labels‘ similarities (by e.g. sorting them into a hierarchy or describing them interms of features (Sanders et al., 2021)). Recently, several methods for representing the similarities between labels have been proposed (Zhang et al., 2018; Wang et al., 2018; Xiong et al., 2021). We here explore and extend the Label Confusion Model (Guo et al., 2021) for learning a representation for discourse relation labels. We explore alternative ways of informing the model about the similarities between relations, by representing relations in terms of their names (and parent category), their typical markers, or in terms of CCR features that describe the relations. Experimental results show that exploiting label similarity improves classification results.

• https://doi.org/10.18653/v1/2023.codi-1.13
• https://doi.org/10.18653/v1/2023.codi-1.13

@inproceedings{varghese-etal-2023-exploiting,
title = {Exploiting Knowledge about Discourse Relations for Implicit Discourse Relation Classification},
author = {Nobel Varghese and Frances Pik Yu Yung and Kaveri Anuranjana and Vera Demberg},
editor = {Michael Strube and Chloe Braud and Christian Hardmeier and Junyi Jessy Li and Sharid Loaiciga and Amir Zeldes},
url = {https://doi.org/10.18653/v1/2023.codi-1.13},
doi = {https://doi.org/10.18653/v1/2023.codi-1.13},
year = {2023},
date = {2023},
booktitle = {Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)},
pages = {99-105},
publisher = {Association for Computational Linguistics},
address = {Toronto, Canada},
abstract = {In discourse relation recognition, the classification labels are typically represented as one-hot vectors. However, the categories are in fact not all independent of one another on the contrary, there are several frameworks that describe the labels' similarities (by e.g. sorting them into a hierarchy or describing them interms of features (Sanders et al., 2021)). Recently, several methods for representing the similarities between labels have been proposed (Zhang et al., 2018; Wang et al., 2018; Xiong et al., 2021). We here explore and extend the Label Confusion Model (Guo et al., 2021) for learning a representation for discourse relation labels. We explore alternative ways of informing the model about the similarities between relations, by representing relations in terms of their names (and parent category), their typical markers, or in terms of CCR features that describe the relations. Experimental results show that exploiting label similarity improves classification results.},
pubstate = {published},
type = {inproceedings}
}

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Developing effective spoken language processing systems for low-resource languages poses several challenges due to the lack of parallel data and limited resources for fine-tuning models. In this work, we target on improving upon both text classification and translation of Nigerian Pidgin (Naija) by collecting a large-scale parallel English-Pidgin corpus and further propose a framework of cross-lingual adaptive training that includes both continual and task adaptive training so as to adapt a base pre-trained model to low-resource languages. Our studies show that English pre-trained language models serve as a stronger prior than multilingual language models on English-Pidgin tasks with up to 2.38 BLEU improvements; and demonstrate that augmenting orthographic data and using task adaptive training with back-translation can have a significant impact on model performance.

• https://arxiv.org/abs/2307.00382

@inproceedings{lin-et-al-2023,
title = {Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin},
author = {Pin-Jie Lin and Muhammed Saeed and Ernie Chang and Merel Scholman},
url = {https://arxiv.org/abs/2307.00382},
year = {2023},
date = {2023},
booktitle = {Proceedings of the 24th INTERSPEECH conference},
abstract = {Developing effective spoken language processing systems for low-resource languages poses several challenges due to the lack of parallel data and limited resources for fine-tuning models. In this work, we target on improving upon both text classification and translation of Nigerian Pidgin (Naija) by collecting a large-scale parallel English-Pidgin corpus and further propose a framework of cross-lingual adaptive training that includes both continual and task adaptive training so as to adapt a base pre-trained model to low-resource languages. Our studies show that English pre-trained language models serve as a stronger prior than multilingual language models on English-Pidgin tasks with up to 2.38 BLEU improvements; and demonstrate that augmenting orthographic data and using task adaptive training with back-translation can have a significant impact on model performance.},
pubstate = {published},
type = {inproceedings}
}

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• https://benjamins.com/catalog/slcs.231.06hoe

@inbook{hoek-scholman-2023,
title = {Expressing non-volitional causality in English},
author = {Jet Hoek and Merel Scholman},
editor = {Łukasz Jędrzejowski and Constanze Fleczoreck},
url = {https://benjamins.com/catalog/slcs.231.06hoe},
year = {2023},
date = {2023},
booktitle = {Micro- and Macro-variation of Causal Clauses: Synchronic and Diachronic Insights},
pages = {167–183},
publisher = {John Benjamins Publishing Company},
address = {Amsterdam},
abstract = {

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• https://doi.org/10.1037/xlm0001270

@article{marchal-etal-2023,
title = {How Statistical Correlations Influence Discourse-Level Processing: Clause Type as a Cue for Discourse Relations},
author = {Marian Marchal and Merel Scholman and Vera Demberg},
url = {https://doi.org/10.1037/xlm0001270},
year = {2023},
date = {2023},
journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},
publisher = {Advance online publication},
abstract = {

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Discourse relations have different patterns of marking across different languages. As a result, discourse connectives are often added, omitted, or rephrased in translation. Prior work has shown a tendency for explicitation of discourse connectives, but such work was conducted using restricted sample sizes due to difficulty of connective identification and alignment. The current study exploits automatic methods to facilitate a large-scale study of connectives in English and German parallel texts. Our results based on over 300 types and 18000 instances of aligned connectives and an empirical approach to compare the cross-lingual specificity gap provide strong evidence of the Explicitation Hypothesis. We conclude that discourse relations are indeed more explicit in translation than texts written originally in the same language. Automatic annotations allow us to carry out translation studies of discourse relations on a large scale. Our methodology using relative entropy to study the specificity of connectives also provides more fine-grained insights into translation patterns.

• 14_Paper     (0.22MB)
• https://aclanthology.org/2023.sigdial-1.2
• https://doi.org/10.18653/v1/2023.sigdial-1.2

@inproceedings{yung-etal-2023-investigating,
title = {Investigating Explicitation of Discourse Connectives in Translation Using Automatic Annotations},
author = {Frances Pik Yu Yung and Merel Scholman and Ekaterina Lapshinova-Koltunski and Christina Pollkl{\"a}sener and Vera Demberg},
editor = {Svetlana Stoyanchev and Shafiq Joty and David Schlangen and Ondrej Dusek and Casey Kennington and Malihe Alikhani},
url = {https://aclanthology.org/2023.sigdial-1.2},
doi = {https://doi.org/10.18653/v1/2023.sigdial-1.2},
year = {2023},
date = {2023},
booktitle = {Proceedings of the 24th Meeting of Special Interest Group on Discourse and Dialogue (SIGDIAL)},
pages = {21-30},
publisher = {Association for Computational Linguistics},
address = {Prague, Czechia},
abstract = {Discourse relations have different patterns of marking across different languages. As a result, discourse connectives are often added, omitted, or rephrased in translation. Prior work has shown a tendency for explicitation of discourse connectives, but such work was conducted using restricted sample sizes due to difficulty of connective identification and alignment. The current study exploits automatic methods to facilitate a large-scale study of connectives in English and German parallel texts. Our results based on over 300 types and 18000 instances of aligned connectives and an empirical approach to compare the cross-lingual specificity gap provide strong evidence of the Explicitation Hypothesis. We conclude that discourse relations are indeed more explicit in translation than texts written originally in the same language. Automatic annotations allow us to carry out translation studies of discourse relations on a large scale. Our methodology using relative entropy to study the specificity of connectives also provides more fine-grained insights into translation patterns.},
pubstate = {published},
type = {inproceedings}
}

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Disagreement in natural language annotation has mostly been studied from a perspective of biases introduced by the annotators and the annotation frameworks. Here, we propose to analyze another source of bias: task design bias, which has a particularly strong impact on crowdsourced linguistic annotations where natural language is used to elicit the interpretation of laymen annotators. For this purpose we look at implicit discourse relation annotation, a task that has repeatedly been shown to be difficult due to the relations‘ ambiguity. We compare the annotations of 1,200 discourse relations obtained using two distinct annotation tasks and quantify the biases of both methods across four different domains. Both methods are natural language annotation tasks designed for crowdsourcing. We show that the task design can push annotators towards certain relations and that some discourse relations senses can be better elicited with one or the other annotation approach. We also conclude that this type of bias should be taken into account when training and testing models.

• 2304.00815     (0.44MB)
• https://arxiv.org/abs/2304.00815

@article{Pyatkinetal.,
title = {Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases introduced by Task Design},
author = {Valentina Pyatkin and Frances Pik Yu Yung and Merel Scholman and Reut Tsarfaty and Ido Dagan and Vera Demberg},
url = {https://arxiv.org/abs/2304.00815},
year = {2023},
date = {2023},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
abstract = {Disagreement in natural language annotation has mostly been studied from a perspective of biases introduced by the annotators and the annotation frameworks. Here, we propose to analyze another source of bias: task design bias, which has a particularly strong impact on crowdsourced linguistic annotations where natural language is used to elicit the interpretation of laymen annotators. For this purpose we look at implicit discourse relation annotation, a task that has repeatedly been shown to be difficult due to the relations' ambiguity. We compare the annotations of 1,200 discourse relations obtained using two distinct annotation tasks and quantify the biases of both methods across four different domains. Both methods are natural language annotation tasks designed for crowdsourcing. We show that the task design can push annotators towards certain relations and that some discourse relations senses can be better elicited with one or the other annotation approach. We also conclude that this type of bias should be taken into account when training and testing models.},
pubstate = {published},
type = {article}
}

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In an eye-tracking-while-reading study, we investigated adult monolinguals’ (N=80) processing of two-clause sentences embedded in short narratives. Three principles theorized to guide comprehension of complex sentences were contrasted: one operating at the clause level, namely clause structure (main clause – subordinate clause or vice versa), and two operating at the discourse-level, namely givenness (given-new vs. new-given) and event order (chronological vs. reverse order). The results indicate that clause structure mainly affects early stages of processing, whereas the two principles operating at the discourse level are more important during later stages and for reading times of the entire sentence. Event order was found to operate relatively independently of the other principles. Givenness was found to overrule clause structure, a phenomenon that can be related to the grounding function of preposed subordinate clauses. We propose a new principle to reflect this interaction effect: the grounding principle.

• https://www.tandfonline.com/doi/full/10.1080/23273798.2022.2077971
• https://doi.org/10.1080/23273798.2022.2077971

@article{Merel_Rules_2022,
title = {Discourse Rules:The Effects of Clause Order Principles on the Reading Process},
author = {Merel Scholman and Liam Blything and Kate Cain and Jacqueline Evers-Vermeul},
url = {https://www.tandfonline.com/doi/full/10.1080/23273798.2022.2077971},
doi = {https://doi.org/10.1080/23273798.2022.2077971},
year = {2022},
date = {2022},
journal = {Language, Cognition and Neuroscience},
pages = {1277-1291},
volume = {37(10)},
abstract = {In an eye-tracking-while-reading study, we investigated adult monolinguals’ (N=80) processing of two-clause sentences embedded in short narratives. Three principles theorized to guide comprehension of complex sentences were contrasted: one operating at the clause level, namely clause structure (main clause - subordinate clause or vice versa), and two operating at the discourse-level, namely givenness (given-new vs. new-given) and event order (chronological vs. reverse order). The results indicate that clause structure mainly affects early stages of processing, whereas the two principles operating at the discourse level are more important during later stages and for reading times of the entire sentence. Event order was found to operate relatively independently of the other principles. Givenness was found to overrule clause structure, a phenomenon that can be related to the grounding function of preposed subordinate clauses. We propose a new principle to reflect this interaction effect: the grounding principle.},
pubstate = {published},
type = {article}
}

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Experimental pragmatics tells us that a metaphor conveys salient features of a vehicle and that highly typical featurestend to be salient. But can highly atypical features also be salient? When asking if John is loyal and hearing “John is afox”, will the hearer conclude that John is disloyal because loyalty is saliently atypical for a fox? This prediction followsfrom our RSA-based model of metaphor understanding which relies on gradient salience. Our behavioral experimentscorroborate the model’s predictions, providing evidence that high and low typicality are salient and result in high in-terpretation confidence and agreement, while average typicality is not salient and makes a metaphor confusing. Ourmodel implements the idea that other features of a vehicle, along with possible alternative vehicles, influence metaphorinterpretation. It produces a significantly better fit compared to an existing RSA model of metaphor understanding,supporting our predictions about the factors at play.

• eScholarship UC item 7kq207zs     (0.07MB)
• https://escholarship.org/uc/item/7kq207zs

@inproceedings{Mayn_2022_of,
title = {Pragmatics of Metaphor Revisited: Modeling the Role of Degree and Salience in Metaphor Understanding},
author = {Alexandra Mayn and Vera Demberg},
url = {https://escholarship.org/uc/item/7kq207zs},
year = {2022},
date = {2022},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society, 43(43)},
pages = {3178ff.},
publisher = {CogSci2022},
abstract = {Experimental pragmatics tells us that a metaphor conveys salient features of a vehicle and that highly typical featurestend to be salient. But can highly atypical features also be salient? When asking if John is loyal and hearing “John is afox”, will the hearer conclude that John is disloyal because loyalty is saliently atypical for a fox? This prediction followsfrom our RSA-based model of metaphor understanding which relies on gradient salience. Our behavioral experimentscorroborate the model’s predictions, providing evidence that high and low typicality are salient and result in high in-terpretation confidence and agreement, while average typicality is not salient and makes a metaphor confusing. Ourmodel implements the idea that other features of a vehicle, along with possible alternative vehicles, influence metaphorinterpretation. It produces a significantly better fit compared to an existing RSA model of metaphor understanding,supporting our predictions about the factors at play.},
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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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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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.

• https://aclanthology.org/2021.acl-short.116.pdf

@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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Cross-linguistic research on discourse structure and coherence marking requires discourse-annotated corpora and connective lexicons in a large number of languages. However, the availability of such resources is limited, especially for languages for which linguistic resources are scarce in general, such as Nigerian Pidgin. In this study, we demonstrate how a semi-automatic approach can be used to source connectives and their relation senses and develop a discourse-annotated corpus in a low-resource language. Connectives and their relation senses were extracted from a parallel corpus combining automatic (PDTB end-to-end parser) and manual annotations. This resulted in Naija-Lex, a lexicon of discourse connectives in Nigerian Pidgin with English translations. The lexicon shows that the majority of Nigerian Pidgin connectives are borrowed from its English lexifier, but that there are also some connectives that are unique to Nigerian Pidgin.

• 2021.codi-main.8     (0.36MB)
• https://aclanthology.org/2021.codi-main.8/
• https://doi.org/10.18653/v1/2021.codi-main.8

@inproceedings{marchal-etal-2021-semi,
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},
url = {https://aclanthology.org/2021.codi-main.8/},
doi = {https://doi.org/10.18653/v1/2021.codi-main.8},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Second Workshop on Computational Approaches to Discourse (CODI 2021)},
pages = {84-94},
publisher = {Association for Computational Linguistics},
address = {Punta Cana, Dominican Republic and Online},
abstract = {Cross-linguistic research on discourse structure and coherence marking requires discourse-annotated corpora and connective lexicons in a large number of languages. However, the availability of such resources is limited, especially for languages for which linguistic resources are scarce in general, such as Nigerian Pidgin. In this study, we demonstrate how a semi-automatic approach can be used to source connectives and their relation senses and develop a discourse-annotated corpus in a low-resource language. Connectives and their relation senses were extracted from a parallel corpus combining automatic (PDTB end-to-end parser) and manual annotations. This resulted in Naija-Lex, a lexicon of discourse connectives in Nigerian Pidgin with English translations. The lexicon shows that the majority of Nigerian Pidgin connectives are borrowed from its English lexifier, but that there are also some connectives that are unique to Nigerian Pidgin.},
pubstate = {published},
type = {inproceedings}
}

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DiscAlign for Penn and RST Discourse Treebanks was developed by Saarland University. It consists of alignment information for the discourse annotations contained in Penn Discourse Treebank Version 2.0 (LDC2008T05) (PDTB 2.0) and RST Discourse Treebank (LDC2002T07) (RST-DT). PDTB 2.0 and RST-DT annotations overlap for 385 newspaper articles in sections 6, 11, 13, 19 and 23 of the Wall Street Journal corpus contained in Treebank-2 (LDC95T7). DiscAlign for Penn and RST Discourse Treebanks contains approximately 6,700 alignments between PDTB 2.0 and RST-DT relations.

• https://catalog.ldc.upenn.edu/LDC2021T16
• https://doi.org/10.35111/cf0q-c454

@miscellaneous{Demberg_etal_DiscAlign,
title = {DiscAlign for Penn and RST Discourse Treebanks},
author = {Vera Demberg and Fatemeh Torabi Asr and Merel Scholman},
url = {https://catalog.ldc.upenn.edu/LDC2021T16},
doi = {https://doi.org/10.35111/cf0q-c454},
year = {2021},
date = {2021},
isbn = {1-58563-975-3},
publisher = {Linguistic Data Consortium},
address = {Philadelphia},
abstract = {DiscAlign for Penn and RST Discourse Treebanks was developed by Saarland University. It consists of alignment information for the discourse annotations contained in Penn Discourse Treebank Version 2.0 (LDC2008T05) (PDTB 2.0) and RST Discourse Treebank (LDC2002T07) (RST-DT). PDTB 2.0 and RST-DT annotations overlap for 385 newspaper articles in sections 6, 11, 13, 19 and 23 of the Wall Street Journal corpus contained in Treebank-2 (LDC95T7). DiscAlign for Penn and RST Discourse Treebanks contains approximately 6,700 alignments between PDTB 2.0 and RST-DT relations.},
pubstate = {published},
type = {miscellaneous}
}

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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.

• pc.20003.cri     (0.43MB)
• https://www.jbe-platform.com/content/journals/10.1075/pc.20003.cri
• https://doi.org/10.1075/pc.20003.cri

@article{Crible2021,
title = {The role of non-connective discourse cues and their interaction with connectives},
author = {Ludivine Crible and Vera Demberg},
url = {https://www.jbe-platform.com/content/journals/10.1075/pc.20003.cri},
doi = {https://doi.org/10.1075/pc.20003.cri},
year = {2021},
date = {2021},
journal = {Pragmatics & Cognition},
pages = {313 - 338},
volume = {27},
number = {2},
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 = {published},
type = {article}
}

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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.

• https://www.frontiersin.org/article/10.3389/fpsyg.2021.660730
• https://doi.org/10.3389/fpsyg.2021.660730

@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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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.

• https://doi.org/10.1515/ling-2021-0011
• https://doi.org/doi:10.1515/ling-2021-0011

@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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