Abstract: Abstract： Judicial Argument Understanding is a practical application of Argument Mining in judicial domain， aiming at mining the interactive argument pair from the arguments of the prosecution and the defense. Argument mining task in judicial domain has the problems of small training samples， long sentence length， and strong domain specialization， etc. Existing models for Judicial Argument Understanding are mostly based on the idea of text classification， and have poor capability of representing the text semantics. To improve the recognition accuracy of the interactive argument pairs， a Judicial Argument Understanding model based on multiplet loss is proposed， which is based on the idea of text matching， matching the prosecutor argument with the defense argument separately for semantic similarity， and realizing the mining of the interactive argument pairs by optimizing the matching degree of the interactive argument pairs. To improve the matching degree of the model for interactive argument pairs， a multivariate group matching loss function is proposed， which further improves the text semantic representation ability by reducing the semantic distance of argument interactive pairs and increasing the semantic distance of non-interactive pairs， so that the semantic distance between arguments can better reflect their interactivity， and the pre-trained model in judicial domain is used as the text semantic representation model. CAIL2022 Judicial Argument Understanding track data was used for testing， and the experimental results showed that the accuracy of the Judicial Argument Understanding model based on multiplet loss function was able to improve by more than 2.04Percentage Points to 85.19% compared with the model using classification ideas， which improved the accuracy of the Judicial Argument Understanding task.

Key words: Key words： multiplet loss, pre-trained models in judicial domain, judicial argument understanding, argument mining, text classification, natural language processing, deep learning