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MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text

arXiv 2212.07156 ↗First posted 14 Dec 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

Modal verbs (e.g., "can", "should", or "must") occur highly frequently in scientific articles. Decoding their function is not straightforward: they are often used for hedging, but they may also denote abilities and restrictions. Understanding their meaning is important for various NLP tasks such as writing assistance or accurate information extraction from scientific text. To foster research on the usage of modals in this genre, we introduce the MIST (Modals In Scientific Text) dataset, which contains 3737 modal instances in five scientific domains annotated for their semantic, pragmatic, or rhetorical function. We systematically evaluate a set of competitive neural architectures on MIST. Transfer experiments reveal that leveraging non-scientific data is of limited benefit for modeling the distinctions in MIST. Our corpus analysis provides evidence that scientific communities differ in their usage of modal verbs, yet, classifiers trained on scientific data generalize to some extent to unseen scientific domains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 78 references that are in the map. First posted on arXiv 14 Dec 2022.

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References · 14

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2 more
  1. 2016Multilingual Modal Sense Classification using a Convolutional Neural Networkcited by 3
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