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

arXiv: https://arxiv.org/abs/2212.07156 · HTML: https://mlsanity.com/paper/2212.07156/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2006.03039/) (2020)
- [Neural models of factuality](https://mlsanity.com/paper/1804.02472/) (2018)
- [The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing](https://mlsanity.com/paper/2106.08037/) (2021)
- [Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures](https://mlsanity.com/paper/1907.03227/) (2019)
- [Modality and Negation in Event Extraction](https://mlsanity.com/paper/2109.09393/) (2021)
- [A Modality Lexicon and its use in Automatic Tagging](https://mlsanity.com/paper/1410.4868/) (2014)
- [Multilingual Modal Sense Classification using a Convolutional Neural Network](https://mlsanity.com/paper/1608.05243/) (2016)
- [Blindness to Modality Helps Entailment Graph Mining](https://mlsanity.com/paper/2109.10227/) (2021)

## 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.