@inproceedings{huo-etal-2023-antcontenttech,
    title = "{A}nt{C}ontent{T}ech at {S}em{E}val-2023 Task 6: Domain-adaptive Pretraining and Auxiliary-task Learning for Understanding {I}ndian Legal Texts",
    author = "Huo, Jingjing  and
      Zhang, Kezun  and
      Liu, Zhengyong  and
      Lin, Xuan  and
      Xu, Wenqiang  and
      Zheng, Maozong  and
      Wang, Zhaoguo  and
      Li, Song",
    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-1.54",
    doi = "10.18653/v1/2023.semeval-1.54",
    pages = "402--408",
    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.",
}

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.
Huo, Jingjing Zhang, Kezun Liu, Zhengyong Lin, Xuan Xu, Wenqiang Zheng, Maozong Wang, Zhaoguo Li, Song
AntContentTech at SemEval-2023 Task 6: Domain-adaptive Pretraining and Auxiliary-task Learning for Understanding Indian Legal Texts