Joint Learning for Legal Text Retrieval and Textual Entailment: Leveraging the Relationship between Relevancy and Affirmation

Nguyen Hai Long, Thi Hai Yen Vuong, Ha Thanh Nguyen, Xuan-Hieu Phan

Abstract
In legal text processing and reasoning, one normally performs information retrieval to find relevant documents of an input question, and then performs textual entailment to answer the question. The former is about relevancy whereas the latter is about affirmation (or conclusion). While relevancy and affirmation are two different concepts, there is obviously a connection between them. That is why performing retrieval and textual entailment sequentially and independently may not make the most of this mutually supportive relationship. This paper, therefore, propose a multi–task learning model for these two tasks to improve their performance. Technically, in the COLIEE dataset, we use the information of Task 4 (conclusions) to improve the performance of Task 3 (searching for legal provisions related to the question). Our empirical findings indicate that this supportive relationship truly exists. This important insight sheds light on how leveraging relationship between tasks can significantly enhance the effectiveness of our multi-task learning approach for legal text processing.
Anthology ID:
2023.nllp-1.19
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Daniel Preoțiuc-Pietro, Catalina Goanta, Ilias Chalkidis, Leslie Barrett, Gerasimos Spanakis, Nikolaos Aletras
Venues:
NLLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
192–201
Language:
URL:
https://aclanthology.org/2023.nllp-1.19
DOI:
10.18653/v1/2023.nllp-1.19
Bibkey:
Cite (ACL):
Nguyen Hai Long, Thi Hai Yen Vuong, Ha Thanh Nguyen, and Xuan-Hieu Phan. 2023. Joint Learning for Legal Text Retrieval and Textual Entailment: Leveraging the Relationship between Relevancy and Affirmation. In Proceedings of the Natural Legal Language Processing Workshop 2023, pages 192–201, Singapore. Association for Computational Linguistics.
Cite (Informal):
Joint Learning for Legal Text Retrieval and Textual Entailment: Leveraging the Relationship between Relevancy and Affirmation (Hai Long et al., NLLP-WS 2023)
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