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. Our code is publicly available here1
Cite
CITATION STYLE
Huo, J., Zhang, K., Liu, Z., Lin, X., Xu, W., Zheng, M., … Li, S. (2023). AntContentTech at SemEval-2023 Task 6: Domain-adaptive Pretraining and Auxiliary-task Learning for Understanding Indian Legal Texts. In 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop (pp. 402–408). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.54
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