Combining similarity and transformer methods for case law entailment

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Abstract

We tackle the complex problem of determining entailment relationships between case law documents, one of the tasks in the Competition on Legal Information Extraction and Entailment (COLIEE). With input of an entailed fragment from a case coupled with a candidate entailing paragraph from a noticed case, our approach relies on four main components: (1) extraction of similarity measures between the two pieces of text; (2) application of a transformer-based technique on the input text; (3) applying a threshold-based classifier; and (4) post-processing the results considering the a priori probability determined by the data distribution on the training samples and combining the results of (1) and (2). Our experiments achieved an F-score of 0.70 on the official COLIEE test dataset, ranking first among all competitors for that task in the 2019 competition.

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Rabelo, J., Kim, M. Y., & Goebel, R. (2019). Combining similarity and transformer methods for case law entailment. In Proceedings of the 17th International Conference on Artificial Intelligence and Law, ICAIL 2019 (pp. 290–296). Association for Computing Machinery, Inc. https://doi.org/10.1145/3322640.3326741

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