Multivalent Entailment Graphs for Question Answering

Nick McKenna, Liane Guillou, Mohammad Javad Hosseini, Sander Bijl de Vroe, Mark Johnson, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding. There has been much progress in unsupervised learning of entailment graphs for this purpose. We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) entails WIN(Biden); (2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task. We show that directional entailment is more helpful for inference than non-directional similarity on questions of fine-grained semantics. We also show that drawing on evidence across valencies answers more questions than by using only the same valency evidence.
Original languageEnglish
Title of host publicationProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
EditorsMarie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Place of PublicationStroudsburg, PA, United States
PublisherACL Anthology
Pages10758-10768
Number of pages11
ISBN (Electronic)978-1-955917-09-4
Publication statusPublished - 7 Nov 2021
Event2021 Conference on Empirical Methods in Natural Language Processing - Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021
https://2021.emnlp.org/

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2021
Country/TerritoryDominican Republic
CityPunta Cana
Period7/11/2111/11/21
Internet address

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