EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification

TY - CHAP

T1 - EXPLAIN, EDIT, GENERATE

T2 - 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

AU - Zhu, Yingjie

AU - Si, Jiasheng

AU - Zhao, Yibo

AU - Zhu, Haiyang

AU - Zhou, Deyu

AU - He, Yulan

N1 - Publisher Copyright: ©2023 Association for Computational Linguistics.

PY - 2023

Y1 - 2023

N2 - Automatic multi-hop fact verification task has gained significant attention in recent years. De spite impressive results, these well-designed models perform poorly on out-of-domain data One possible solution is to augment the training data with counterfactuals, which are generated by minimally altering the causal features of the original data. However, current counterfac tual data augmentation techniques fail to handle multi-hop fact verification due to their incapa bility to preserve the complex logical relation ships within multiple correlated texts. In this paper, we overcome this limitation by devel oping a rationale-sensitive method to generate linguistically diverse and label-flipping counter factuals while preserving logical relationships In specific, the diverse and fluent counterfactu als are generated via an Explain-Edit-Generate architecture. Moreover, the checking and fil tering modules are proposed to regularize the counterfactual data with logical relations and flipped labels. Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically di verse counterfactual data without disrupting their logical relationships.

AB - Automatic multi-hop fact verification task has gained significant attention in recent years. De spite impressive results, these well-designed models perform poorly on out-of-domain data One possible solution is to augment the training data with counterfactuals, which are generated by minimally altering the causal features of the original data. However, current counterfac tual data augmentation techniques fail to handle multi-hop fact verification due to their incapa bility to preserve the complex logical relation ships within multiple correlated texts. In this paper, we overcome this limitation by devel oping a rationale-sensitive method to generate linguistically diverse and label-flipping counter factuals while preserving logical relationships In specific, the diverse and fluent counterfactu als are generated via an Explain-Edit-Generate architecture. Moreover, the checking and fil tering modules are proposed to regularize the counterfactual data with logical relations and flipped labels. Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically di verse counterfactual data without disrupting their logical relationships.

UR - http://www.scopus.com/inward/record.url?scp=85184808312&partnerID=8YFLogxK

M3 - Conference paper

AN - SCOPUS:85184808312

T3 - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings

SP - 13377

EP - 13392

BT - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings

A2 - Bouamor, Houda

A2 - Pino, Juan

A2 - Bali, Kalika

PB - Association for Computational Linguistics (ACL)

Y2 - 6 December 2023 through 10 December 2023

ER -