Overview of SMP-CAIL2020-Argmine:The Interactive Argument-Pair Extraction in Judgement Document Challenge  

作　　者：Jian Yuan Zhongyu Wei Yixu Gao Wei Chen Yun Song Donghua Zhao Jinglei Ma Zhen Hu Shaokun Zou Donghai Li Xuanjing Huang 

机构地区：[1]School of Data Science,Fudan University,Shanghai 200433,China [2]Heilongjiang University,Heilongjiang 150080,China [3]School of Mathematical Sciences,Fudan University,Shanghai 200433,China [4]China Judicial Big Data Institute,Beijing 100043,China [5]THUNISOFT Co.,Ltd.,Beijing 100084,China [6]Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China [7]School of Computer Science,Fudan University,Shanghai 200433,China

出　　处：《Data Intelligence》2021年第2期287-307,共21页数据智能（英文）

基　　金：supported by National Key Research and Development Plan(No.2018YFC0830600),and is cooperated with China Justice Big Data Institute,which provided judgement documents and the employment of professional annotators.The competition is also sponsored by Beijing Thunisoft Information Technology Co.,Ltd.,and supported by both CAIL and SMP organizers.

摘　　要：In this paper we present the results of the Interactive Argument-Pair Extraction in Judgement Document Challenge 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.

关 键 词：Argumentation mining Judgement documents Natural language understanding Pretrained language model 

分 类 号：D916[政治法律—法学] TP18[自动化与计算机技术—控制理论与控制工程]