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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.
|Title of host publication||Proceedings of the Second Workshop on Insights from Negative Results in NLP|
|Editors||João Sedoc, Anna Rogers, Anna Rumshisky, Shabnam Tafreshi|
|Place of Publication||Stroudsburg, PA, United States|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||7|
|Publication status||Published - 10 Nov 2021|
|Event||Workshop on Insights from Negative Results in NLP - Punta Cana, Dominican Republic|
Duration: 10 Nov 2021 → 10 Nov 2021
|Workshop||Workshop on Insights from Negative Results in NLP|
|Period||10/11/21 → 10/11/21|
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1/08/17 → 31/01/23