@inproceedings{mckenna-etal-2021-multivalent,
title = "Multivalent Entailment Graphs for Question Answering",
author = "McKenna, Nick and
Guillou, Liane and
Hosseini, Mohammad Javad and
Bijl de Vroe, Sander and
Johnson, Mark and
Steedman, Mark",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.840/",
doi = "10.18653/v1/2021.emnlp-main.840",
pages = "10758--10768",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multivalent Entailment Graphs for Question Answering
%A McKenna, Nick
%A Guillou, Liane
%A Hosseini, Mohammad Javad
%A Bijl de Vroe, Sander
%A Johnson, Mark
%A Steedman, Mark
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F mckenna-etal-2021-multivalent
%X 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.
%R 10.18653/v1/2021.emnlp-main.840
%U https://aclanthology.org/2021.emnlp-main.840/
%U https://doi.org/10.18653/v1/2021.emnlp-main.840
%P 10758-10768
Markdown (Informal)
[Multivalent Entailment Graphs for Question Answering](https://aclanthology.org/2021.emnlp-main.840/) (McKenna et al., EMNLP 2021)
ACL
- Nick McKenna, Liane Guillou, Mohammad Javad Hosseini, Sander Bijl de Vroe, Mark Johnson, and Mark Steedman. 2021. Multivalent Entailment Graphs for Question Answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10758–10768, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.