Abstract: In this paper we present the results of the Interactive Argument-Pair Extraction in Judgement Document Competition held by both the Chinese AI and Law Challenge (CAIL) and the Chinese National Social Media Processing Conference (SMP), and introduce the related data set – SMP-CAIL2020-Argmine. The task challenged participants to choose the correct argument among five candidates proposed by the defense to refute or acknowledge the given argument made by the plaintiff, providing the full context recorded in the judgement documents of both parties. We received entries from 63 competing teams, 38 of which scored higher than the provided baseline model (BERT) in the first phase and entered the second phase. The best performing system in the two phases achieved accuracy of 0.856 and 0.905, respectively. In this paper, we will present the results of the competition and a summary of the systems, highlighting commonalities and innovations among participating systems. The SMP-CAIL2020-Argmine data set and baseline models have been already released.
Keywords: Argumentation Mining; Judgement Documents; Natural Language Understanding; Pretrained Language Model