Abstract
Competition on Legal Information Extraction/Entailment (COLIEE) is an annual competition associated with the International Workshop in Juris-Informatics. The challenge for this competition is required not only the skills in processing long documents but also the ability to resolve ambiguity in the legal domain. For lengthy documents, we proposed a document-level attention mechanism (Task 1) and passage mining (Task 3, 4). Regarding ambiguity in the legal domain, we propose 2 methods that use abstract meaning representation to remove noise in given query and candidate documents (Task 2), and the second approach is use-case identification (Task 3). By categorizing the given query, we have different approaches to solving questions. The results reflect the difficulty level and competitiveness in this competition.
Supported by JSPS Kakenhi Grant Number 20H04295, 20K20406, and 20K20625.
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References
Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer (2020). arXiv:2004.05150
Brown, T., et al.: Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020)
Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: the muppets straight out of law school (2020). arXiv preprint arXiv:2010.02559
Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: the muppets straight out of law school. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2898–2904, Online, November 2020. Association for Computational Linguistics (2020)
Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. In: ICLR (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2018). arXiv preprint arXiv:1810.04805
Fujita, M., Kiyota, N., Kano, Y.: Predicate’s argument resolver and entity abstraction for legal question answering: kis teams at COLIEE 2021 shared task. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law (2021)
Kim, M.Y., Rabelo, J., Goebel, R.: Bm25 and transformer- based legal information extraction and entailment. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law (2021)
Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: International Conference on Machine Learning, pp. 957–966. PMLR (2015)
Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach (2019). CoRR, abs/1907.11692
Nguyen, H.-T., et al.: JNLP team: deep learning approaches for legal processing tasks in COLIEE 2021 (2021). arXiv preprint arXiv:2106.13405
Rabelo, J., Kim, M.-Y., Goebel, R., Yoshioka, M., Kano, Y., Satoh, K.: COLIEE 2020: methods for legal document retrieval and entailment. In: Okazaki, N., Yada, K., Satoh, K., Mineshima, K. (eds.) JSAI-isAI 2020. LNCS (LNAI), vol. 12758, pp. 196–210. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79942-7_13
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer (2019). arXiv preprint arXiv:1910.10683
Robertson, S.E., et al.: Okapi at TREC-3. Nist Special Publication Sp, 109, p. 109 (1995)
Schilder, F., et al.: A pentapus grapples with legal reasoning. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law (2021)
Shao, Y., et al.: BERT-PLI: modeling paragraph-level interactions for legal case retrieval. In: IJCAI, pp. 3501–3507 (2020)
Strohman, T., Metzler, D., Turtle, H., Croft, W.B.: Indri: A language model-based search engine for complex queries. In: Proceedings of the International Conference on Intelligent Analysis, vol. 2, pp. 2–6. Citeseer (2005)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Wehnert, S., Sudhi, V., Dureja, S., Kutty, L., Shahania, S., De Luca, E.W.: Legal norm retrieval with variations of the Bert model combined with TF-IDF vectorization. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pp. 285–294 (2021)
Yoshioka, M., Aoki, Y., Suzuki, Y.: BERT-based ensemble methods with data augmentation for legal textual entailment in COLIEE statute law task. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pp. 278–284 (2021)
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Bui, Q.M. et al. (2023). JNLP Team: Deep Learning Approaches for Tackling Long and Ambiguous Legal Documents in COLIEE 2022. In: Takama, Y., Yada, K., Satoh, K., Arai, S. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2022. Lecture Notes in Computer Science(), vol 13859. Springer, Cham. https://doi.org/10.1007/978-3-031-29168-5_5
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