# The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing

arXiv: https://arxiv.org/abs/2106.08037 · HTML: https://mlsanity.com/paper/2106.08037/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [NCRF++: An Open-source Neural Sequence Labeling Toolkit](https://mlsanity.com/paper/1806.05626/) (2018)
- [Neural models of factuality](https://mlsanity.com/paper/1804.02472/) (2018)
- [Use of Modality and Negation in Semantically-Informed Syntactic MT](https://mlsanity.com/paper/1502.01682/) (2015)
- [Multilingual Modal Sense Classification using a Convolutional Neural Network](https://mlsanity.com/paper/1608.05243/) (2016)

## Built on by (most cited first, 13 of 13)

- [Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models](https://mlsanity.com/paper/2302.13439/) (2023)
- [Multi-VALUE: A Framework for Cross-Dialectal English NLP](https://mlsanity.com/paper/2212.08011/) (2022)
- [LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements](https://mlsanity.com/paper/2404.06283/) (2024)
- [Agent-Specific Deontic Modality Detection in Legal Language](https://mlsanity.com/paper/2211.12752/) (2022)
- [A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement](https://mlsanity.com/paper/2404.04035/) (2024)
- [Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification](https://mlsanity.com/paper/2308.07876/) (2023)
- [MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text](https://mlsanity.com/paper/2212.07156/) (2022)
- [Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets](https://mlsanity.com/paper/2212.10152/) (2022)
- [Proposition from the Perspective of Chinese Language: A Chinese Proposition Classification Evaluation Benchmark](https://mlsanity.com/paper/2309.09602/) (2023)
- [Plausibility Vaccine: Injecting LLM Knowledge for Event Plausibility](https://mlsanity.com/paper/2503.12667/) (2025)
- [Measuring and Analyzing Subjective Uncertainty in Scientific Communications](https://mlsanity.com/paper/2503.21114/) (2025)
- [Modelling Adjectival Modification Effects on Semantic Plausibility](https://mlsanity.com/paper/2507.21828/) (2025)
- [An Annotation Scheme for Factuality and its Application to Parliamentary Proceedings](https://mlsanity.com/paper/2509.26406/) (2025)

## Abstract (arXiv)

Modality is the linguistic ability to describe events with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, but also improves the detection of modal events in their own right.