# Blindness to Modality Helps Entailment Graph Mining

arXiv: https://arxiv.org/abs/2109.10227 · HTML: https://mlsanity.com/paper/2109.10227/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 21 Sep 2021.

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

- [Probabilistic Models of Relational Implication](https://mlsanity.com/paper/1907.12048/) (2019)
- [Multivalent Entailment Graphs for Question Answering](https://mlsanity.com/paper/2104.07846/) (2021)
- [Exploring Different Dimensions of Attention for Uncertainty Detection](https://mlsanity.com/paper/1612.06549/) (2016)
- [Incorporating Temporal Information in Entailment Graph Mining](https://mlsanity.com/paper/2109.09412/) (2021)
- [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)

## Built on by (most cited first, 2 of 2)

- [Smoothing Entailment Graphs with Language Models](https://mlsanity.com/paper/2208.00318/) (2022)
- [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)

## Abstract (arXiv)

Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also be expected to benefit from attention to modality. We build Entailment Graphs using a news corpus filtered with a modality parser, and show that stripping modal modifiers from predicates in fact increases performance. This suggests that for some tasks, the pragmatics of modal modification of predicates allows them to contribute as evidence of entailment.