@inproceedings{ningthoujam-etal-2023-researchteam,
title = "{R}esearch{T}eam{\_}{HCN} at {S}em{E}val-2023 Task 6: A knowledge enhanced transformers based legal {NLP} system",
author = "Ningthoujam, Dhanachandra and
Patel, Pinal and
Kareddula, Rajkamal and
Vangipuram, Ramanand",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.173",
doi = "10.18653/v1/2023.semeval-1.173",
pages = "1245--1253",
abstract = "This paper presents our work on LegalEval (understanding legal text), one of the tasks in SemEval-2023. It comprises of three sub-tasks namely Rhetorical Roles (RR), Legal Named Entity Recognition (L-NER), and Court Judge- ment Prediction with Explanation (CJPE). We developed different deep-learning models for each sub-tasks. For RR, we developed a multi- task learning model with contextual sequential sentence classification as the main task and non- contextual single sentence prediction as the sec- ondary task. Our model achieved an F1-score of 76.50{\%} on the unseen test set, and we at- tained the 14th position on the leaderboard. For the L-NER problem, we have designed a hybrid model, consisting of a multi-stage knowledge transfer learning framework and a rule-based system. This model achieved an F1-score of 91.20{\%} on the blind test set and attained the top position on the final leaderboard. Finally, for the CJPE task, we used a hierarchical ap- proach and could get around 66.67{\%} F1-score on judgment prediction and 45.83{\%} F1-score on the explainability of the CJPE task, and we attained 8th position on the leaderboard for this sub-task.",
}
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<abstract>This paper presents our work on LegalEval (understanding legal text), one of the tasks in SemEval-2023. It comprises of three sub-tasks namely Rhetorical Roles (RR), Legal Named Entity Recognition (L-NER), and Court Judge- ment Prediction with Explanation (CJPE). We developed different deep-learning models for each sub-tasks. For RR, we developed a multi- task learning model with contextual sequential sentence classification as the main task and non- contextual single sentence prediction as the sec- ondary task. Our model achieved an F1-score of 76.50% on the unseen test set, and we at- tained the 14th position on the leaderboard. For the L-NER problem, we have designed a hybrid model, consisting of a multi-stage knowledge transfer learning framework and a rule-based system. This model achieved an F1-score of 91.20% on the blind test set and attained the top position on the final leaderboard. Finally, for the CJPE task, we used a hierarchical ap- proach and could get around 66.67% F1-score on judgment prediction and 45.83% F1-score on the explainability of the CJPE task, and we attained 8th position on the leaderboard for this sub-task.</abstract>
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%0 Conference Proceedings
%T ResearchTeam_HCN at SemEval-2023 Task 6: A knowledge enhanced transformers based legal NLP system
%A Ningthoujam, Dhanachandra
%A Patel, Pinal
%A Kareddula, Rajkamal
%A Vangipuram, Ramanand
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ningthoujam-etal-2023-researchteam
%X This paper presents our work on LegalEval (understanding legal text), one of the tasks in SemEval-2023. It comprises of three sub-tasks namely Rhetorical Roles (RR), Legal Named Entity Recognition (L-NER), and Court Judge- ment Prediction with Explanation (CJPE). We developed different deep-learning models for each sub-tasks. For RR, we developed a multi- task learning model with contextual sequential sentence classification as the main task and non- contextual single sentence prediction as the sec- ondary task. Our model achieved an F1-score of 76.50% on the unseen test set, and we at- tained the 14th position on the leaderboard. For the L-NER problem, we have designed a hybrid model, consisting of a multi-stage knowledge transfer learning framework and a rule-based system. This model achieved an F1-score of 91.20% on the blind test set and attained the top position on the final leaderboard. Finally, for the CJPE task, we used a hierarchical ap- proach and could get around 66.67% F1-score on judgment prediction and 45.83% F1-score on the explainability of the CJPE task, and we attained 8th position on the leaderboard for this sub-task.
%R 10.18653/v1/2023.semeval-1.173
%U https://aclanthology.org/2023.semeval-1.173
%U https://doi.org/10.18653/v1/2023.semeval-1.173
%P 1245-1253
Markdown (Informal)
[ResearchTeam_HCN at SemEval-2023 Task 6: A knowledge enhanced transformers based legal NLP system](https://aclanthology.org/2023.semeval-1.173) (Ningthoujam et al., SemEval 2023)
ACL