Combining Similarity and Transformer Methods for Case Law Entailment

By Juliano Rabelo, Mi-Young Kim, Randy Goebel. 2019

31 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Political Science and International Relations

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Citations in the corpus

A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering (2020, unknown): “Many papers explore case-based reasoning for law, with expert systems [42, 55], human annotations [7] or automatic annotations [3] as well as transformer-based methods [43].”
A Summary of the COLIEE 2019 Competition (2019, methodology): “– UA (three runs) [19] proposes an approach which relies the extraction of similarity measures between the candidate paragraph and the entailed fragment; the application of BERT on those two pieces of text; use of a threshold-based classifier; and post-processing the results considering the a priori”