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Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Modal verbs, such as can, may, and must, are commonly used in daily communication to convey the speaker’s perspective related to the likelihood and/or mode of the proposition. They can differ greatly in meaning depending on how they’re used and the context of a sentence (e.g. “They must work together.” vs. “They must have worked together.”). Despite their practical importance in natural language understanding, linguists have yet to agree on a single, prominent framework for the categorization of modal verb senses. This lack of agreement stems from high degrees of flexibility and pol-ysemy from the modal verbs, making it more difficult for researchers to incorporate insights from this family of words into their work. As a tool to help navigate this issue, this work presents MoVerb, a dataset consisting of 27,240 annotations of modal verb senses over 4,540 utterances containing one or more sentences from social conversations. Each utterance is annotated by three annotators using two different theoretical frameworks (i.e., Quirk and Palmer) of modal verb senses. We observe that both frameworks have similar inter-annotator agreements, despite having a different number of sense labels (eight for Quirk and three for Palmer). With RoBERTa-based classifiers fine-tuned on MoVerb, we achieve F1 scores of 82.2 and 78.3 on Quirk and Palmer, respectively, showing that modal verb sense disambiguation is not a trivial task.

Original languageEnglish (US)
Title of host publicationCoNLL 2023 - 27th Conference on Computational Natural Language Learning, Proceedings
EditorsJing Jiang, David Reitter, Shumin Deng
PublisherAssociation for Computational Linguistics (ACL)
Pages183-199
Number of pages17
ISBN (Electronic)9798891760394
DOIs
StatePublished - 2023
Event27th Conference on Computational Natural Language Learning, CoNLL 2023 held alongside EMNLP 2023 - Singapore, Singapore
Duration: Dec 6 2023Dec 7 2023

Publication series

NameCoNLL 2023 - 27th Conference on Computational Natural Language Learning, Proceedings

Conference

Conference27th Conference on Computational Natural Language Learning, CoNLL 2023 held alongside EMNLP 2023
Country/TerritorySingapore
CitySingapore
Period12/6/2312/7/23

Bibliographical note

Publisher Copyright:
© 2023 CoNLL 2023 - 27th Conference on Computational Natural Language Learning, Proceedings. All rights reserved.

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