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            <namePart type="given">Marie-Francine</namePart>
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    <abstract>Finding counterevidence to statements is key to many tasks, including counterargument generation. We build a system that, given a statement, retrieves counterevidence from diverse sources on the Web. At the core of this system is a natural language inference (NLI) model that determines whether a candidate sentence is valid counterevidence or not. Most NLI models to date, however, lack proper reasoning abilities necessary to find counterevidence that involves complex inference. Thus, we present a knowledge-enhanced NLI model that aims to handle causality- and example-based inference by incorporating knowledge graphs. Our NLI model outperforms baselines for NLI tasks, especially for instances that require the targeted inference. In addition, this NLI model further improves the counterevidence retrieval system, notably finding complex counterevidence better.</abstract>
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%0 Conference Proceedings
%T Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
%A Jo, Yohan
%A Yoo, Haneul
%A Bak, JinYeong
%A Oh, Alice
%A Reed, Chris
%A Hovy, Eduard
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F jo-etal-2021-knowledge-enhanced
%X Finding counterevidence to statements is key to many tasks, including counterargument generation. We build a system that, given a statement, retrieves counterevidence from diverse sources on the Web. At the core of this system is a natural language inference (NLI) model that determines whether a candidate sentence is valid counterevidence or not. Most NLI models to date, however, lack proper reasoning abilities necessary to find counterevidence that involves complex inference. Thus, we present a knowledge-enhanced NLI model that aims to handle causality- and example-based inference by incorporating knowledge graphs. Our NLI model outperforms baselines for NLI tasks, especially for instances that require the targeted inference. In addition, this NLI model further improves the counterevidence retrieval system, notably finding complex counterevidence better.
%R 10.18653/v1/2021.findings-emnlp.264
%U https://aclanthology.org/2021.findings-emnlp.264
%U https://doi.org/10.18653/v1/2021.findings-emnlp.264
%P 3074-3094

Markdown (Informal)

[Knowledge-Enhanced Evidence Retrieval for Counterargument Generation](https://aclanthology.org/2021.findings-emnlp.264) (Jo et al., Findings 2021)

• Knowledge-Enhanced Evidence Retrieval for Counterargument Generation (Jo et al., Findings 2021)

ACL

• Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed, and Eduard Hovy. 2021. Knowledge-Enhanced Evidence Retrieval for Counterargument Generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3074–3094, Punta Cana, Dominican Republic. Association for Computational Linguistics.