Case-Based Abductive Natural Language Inference

Marco Valentino, Mokanarangan Thayaparan, André Freitas


Abstract
Most of the contemporary approaches for multi-hop Natural Language Inference (NLI) construct explanations considering each test case in isolation. However, this paradigm is known to suffer from semantic drift, a phenomenon that causes the construction of spurious explanations leading to wrong conclusions. In contrast, this paper proposes an abductive framework for multi-hop NLI exploring the retrieve-reuse-refine paradigm in Case-Based Reasoning (CBR). Specifically, we present Case-Based Abductive Natural Language Inference (CB-ANLI), a model that addresses unseen inference problems by analogical transfer of prior explanations from similar examples. We empirically evaluate the abductive framework on commonsense and scientific question answering tasks, demonstrating that CB-ANLI can be effectively integrated with sparse and dense pre-trained encoders to improve multi-hop inference, or adopted as an evidence retriever for Transformers. Moreover, an empirical analysis of semantic drift reveals that the CBR paradigm boosts the quality of the most challenging explanations, a feature that has a direct impact on robustness and accuracy in downstream inference tasks.
Anthology ID:
2022.coling-1.134
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1556–1568
Language:
URL:
https://aclanthology.org/2022.coling-1.134
DOI:
Bibkey:
Cite (ACL):
Marco Valentino, Mokanarangan Thayaparan, and André Freitas. 2022. Case-Based Abductive Natural Language Inference. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1556–1568, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Case-Based Abductive Natural Language Inference (Valentino et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.134.pdf
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