Abstract: In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the Supreme People's Court of China. CAIL2019-SCM focuses on detecting similar cases, and the participants are required to check which two cases are more similar in the triplets. There are 711 teams who participated in this year's competition, and the best team has reached a score of 71.88. We have also implemented several baselines to help researchers better understand this task. The dataset and more details can be found from this https URL.

Introduction: Similar Case Matching (SCM) plays a major role in legal system, especially in common law legal system. The most similar cases in the past determine the judgment results of cases in common law systems. As a result, legal professionals often spend much time finding and judging similar cases to prove fairness in judgment. As automatically finding similar cases can benefit to the legal system, we select SCM as one of the tasks of CAIL2019. Chinese AI and Law Challenge (CAIL) is a competition of applying artificial intelligence technology to legal tasks. The goal of the competition is to use AI to help the legal system. CAIL was first held in 2018, and the main task of CAIL2018 BIBREF0, BIBREF1 is predicting the judgment results from the fact description. The judgment results include the accusation, applicable articles, and the term of penalty. CAIL2019 contains three different tasks, including Legal Question-Answering, Legal Case Element Prediction, and Similar Case Matching. Furthermore, we will focus on SCM in this paper. More specifically, CAIL2019-SCM contains 8,964 triplets of legal documents. Every legal documents is collected from China Judgments Online. In order to ensure the similarity of the cases in one triplet, all selected documents are related to Private Lending. Every document in the triplet contains the fact description. CAIL2019-SCM requires researchers to decide which two cases are more similar in a triplet. By detecting similar cases in triplets, we can apply this algorithm for ranking all documents to find the most similar document in the database. There are 247 teams who have participated CAIL2019-SCM, and the best team has reached a score of $71.88$, which is about 20 points higher than the baseline. The results show that the existing methods have made great progress on this task, but there is still much room for improvement. In other words, CAIL2019-SCM can benefit the research of legal case matching. Furthermore, there are several main challenges of CAIL2019-SCM: (1) The difference between documents may be small, and then it is hard to decide which two documents are more similar. Moreover, the similarity is defined by legal workers. We must utilize legal knowledge into this task rather than calculate similarity on the lexical level. (2) The length of the documents is quite long. Most documents contain more than 512 characters, and then it is hard for existing methods to capture document level information. In the following parts, we will give more details about CAIL2019-SCM, including related works about SCM, the task definition, the construction of the dataset, and several experiments on the dataset.

Question: What semantic features help in detecting whether a piece of text is genuine or generated? of 
Answer: No feature is given, only discussion that semantic features are use in practice and yet to be discovered how to embed that knowledge into statistical decision theory framework.