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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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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