@inproceedings{rusnachenko-etal-2023-nclu,
    title = "nclu{\_}team at {S}em{E}val-2023 Task 6: Attention-based Approaches for Large Court Judgement Prediction with Explanation",
    author = "Rusnachenko, Nicolay  and
      Markchom, Thanet  and
      Liang, Huizhi",
    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-1.36",
    doi = "10.18653/v1/2023.semeval-1.36",
    pages = "270--274",
    abstract = "Legal documents tend to be large in size. In this paper, we provide an experiment with attention-based approaches complemented by certain document processing techniques for judgment prediction. For the prediction of explanation, we consider this as an extractive text summarization problem based on an output of (1) CNN with attention mechanism and (2) self-attention of language models. Our extensive experiments show that treating document endings at first results in a 2.1{\%} improvement in judgment prediction across all the models. Additional content peeling from non-informative sentences allows an improvement of explanation prediction performance by 4{\%} in the case of attention-based CNN models. The best submissions achieved 8{'}th and 3{'}rd ranks on judgment prediction (C1) and prediction with explanation (C2) tasks respectively among 11 participating teams. The results of our experiments are published",
}

Legal documents tend to be large in size. In this paper, we provide an experiment with attention-based approaches complemented by certain document processing techniques for judgment prediction. For the prediction of explanation, we consider this as an extractive text summarization problem based on an output of (1) CNN with attention mechanism and (2) self-attention of language models. Our extensive experiments show that treating document endings at first results in a 2.1{\%} improvement in judgment prediction across all the models. Additional content peeling from non-informative sentences allows an improvement of explanation prediction performance by 4{\%} in the case of attention-based CNN models. The best submissions achieved 8{'}th and 3{'}rd ranks on judgment prediction (C1) and prediction with explanation (C2) tasks respectively among 11 participating teams. The results of our experiments are published
Rusnachenko, Nicolay Markchom, Thanet Liang, Huizhi
nclu_team at SemEval-2023 Task 6: Attention-based Approaches for Large Court Judgement Prediction with Explanation