@inproceedings{hai-long-etal-2023-joint,
title = "Joint Learning for Legal Text Retrieval and Textual Entailment: Leveraging the Relationship between Relevancy and Affirmation",
author = "Hai Long, Nguyen and
Vuong, Thi Hai Yen and
Nguyen, Ha Thanh and
Phan, Xuan-Hieu",
editor = "Preoțiuc-Pietro, Daniel and
Goanta, Catalina and
Chalkidis, Ilias and
Barrett, Leslie and
Spanakis, Gerasimos and
Aletras, Nikolaos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nllp-1.19/",
doi = "10.18653/v1/2023.nllp-1.19",
pages = "192--201",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Joint Learning for Legal Text Retrieval and Textual Entailment: Leveraging the Relationship between Relevancy and Affirmation
%A Hai Long, Nguyen
%A Vuong, Thi Hai Yen
%A Nguyen, Ha Thanh
%A Phan, Xuan-Hieu
%Y Preoțiuc-Pietro, Daniel
%Y Goanta, Catalina
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Spanakis, Gerasimos
%Y Aletras, Nikolaos
%S Proceedings of the Natural Legal Language Processing Workshop 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hai-long-etal-2023-joint
%X 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.
%R 10.18653/v1/2023.nllp-1.19
%U https://aclanthology.org/2023.nllp-1.19/
%U https://doi.org/10.18653/v1/2023.nllp-1.19
%P 192-201
Markdown (Informal)
[Joint Learning for Legal Text Retrieval and Textual Entailment: Leveraging the Relationship between Relevancy and Affirmation](https://aclanthology.org/2023.nllp-1.19/) (Hai Long et al., NLLP 2023)
ACL