AntContentTech at SemEval-2023 Task 6: Domain-adaptive Pretraining and Auxiliary-task Learning for Understanding Indian Legal Texts
Jingjing Huo, Kezun Zhang, Zhengyong Liu, Xuan Lin, Wenqiang Xu, Maozong Zheng, Zhaoguo Wang, Song Li
Abstract
The objective of this shared task is to gain an understanding of legal texts, and it is beset with difficulties such as the comprehension of lengthy noisy legal documents, domain specificity as well as the scarcity of annotated data. To address these challenges, we propose a system that employs a hierarchical model and integrates domain-adaptive pretraining, data augmentation, and auxiliary-task learning techniques. Moreover, to enhance generalization and robustness, we ensemble the models that utilize these diverse techniques. Our system ranked first on the RR sub-task and in the middle for the other two sub-tasks.- Anthology ID:
- 2023.semeval-1.54
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 402–408
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.54
- DOI:
- 10.18653/v1/2023.semeval-1.54
- Cite (ACL):
- Jingjing Huo, Kezun Zhang, Zhengyong Liu, Xuan Lin, Wenqiang Xu, Maozong Zheng, Zhaoguo Wang, and Song Li. 2023. AntContentTech at SemEval-2023 Task 6: Domain-adaptive Pretraining and Auxiliary-task Learning for Understanding Indian Legal Texts. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 402–408, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- AntContentTech at SemEval-2023 Task 6: Domain-adaptive Pretraining and Auxiliary-task Learning for Understanding Indian Legal Texts (Huo et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.semeval-1.54.pdf