Knowledge-Enhanced Evidence Retrieval for Counterargument Generation

Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed, Eduard Hovy


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.
Anthology ID:
2021.findings-emnlp.264
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3074–3094
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.264
DOI:
10.18653/v1/2021.findings-emnlp.264
Bibkey:
Cite (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.
Cite (Informal):
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation (Jo et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.264.pdf
Code
 yohanjo/kenli
Data
ANLIFEVERSNLI