CDD : A Large Scale Dataset for Legal Intelligence Research (2023)

Abstract: As an important application of Artificial Intelligence, legal intelligence has recently attracted the attention of many researchers. Previous works investigated diverse issues like predicting crimes, predicting outcomes of judicial debates, or extracting information/knowledge from various kinds of legal documents. Although many advances have been made, the research on supporting prediction of court judgments remains relatively scarce, while the lack of large-scale data resources limits the development of this research.In this paper, we present a novel, large-size Court Debate Dataset (CDD), which includes 30,481 court cases, totaling 1,144,425 utterances. CDD contains real-world conversations involving judges, plaintiffs and defendants in court trials. To construct this dataset we have invited experienced judges to design appropriate labels for data records. We then asked law school students to provide annotations based on the defined labels. The dataset can be applied to several downstream tasks, such as text summarization, dialogue generation, text classification, etc. We introduce the details of the different tasks in the rapidly developing field of legal intelligence, the research of which can be fostered thanks to our dataset, and we provide the corresponding benchmark performance.

## introduction the increasing needs for efficient, high quality judicial service and the shortage of judicial personnel have become important concerns in the current society. the use of artificial intelligence (ai) technology to assist judges in effectively adjudicating cases is a research area that has potential to help improve judicial efficiency. in the real world, legal intelligence (li) @xcite could be applied in many scenarios, such as supporting management of court trials, legal judgment prediction, case information extraction, etc. the use of artificial intelli-gence technology to provide judicial services could not only alleviate the pressure on judges, but might also improve the efficiency of delivering judicial decisions. in the recent years, judicial intelligence has gradually entered into the field of interest of many researchers, resulting in some explorations in this field ranging from legal judgment prediction @xcite , analyzing trial cases, predicting particular laws that apply to a given case @xcite , through court trialing to predicting the type of committed crimes. the advancement of legal intelligence research is however closely related to the availability of public datasets. two well-known public available datasets that are currently in use are especially worth mentioning here: cail and echr. chinese ai and law challenge (cail) @xcite ) contains more than 2.6 million verdicts of criminal cases published by the supreme people's court of china foot_0 , where each verdict consists of the identified facts given by the judge and the applicable law articles, charges, and prison terms, supporting the task of judgment prediction. echr @xcite , on the other hand, is the first english legal judgment prediction dataset, containing cases from the european court of human rights. although previous research has made significant progress on the track of judgment prediction, the lack of effective and diverse datasets has become a considerable obstacle to the development of the legal intelligence field. legal intelligence involves a wide range of scenarios and is not just limited to legal judgment prediction or crime prediction. it can provide judges with more efficient and transparent trials in more ways. in this context, we provide a large-scale judicial dataset, which contains the real-world dia- ## related work legal intelligence research has been initiated in 1960s @xcite . @xcite proposed the use of algebraic calculations to determine the judgement of the court case. especially, in the recent years, legal intelligence has emerged as a popular topic attracting attention of many researchers @xcite @xcite @xcite . @xcite proposed to predict the outcome of trials based on the facts of the judicial cases. @xcite introduced a topological multi-task learning framework (top-judge) that incorporates multi-task learning and dag dependencies into judgment prediction. @xcite leveraged multi-view dispute representation for e-commerce judgement result prediction while @xcite utilized fact, law and article information to build a hierarchical matching network for crime classification. @xcite extracted objective elements from factual descriptions for crime prediction. the release of relevant datasets often provides important stimuli for a field. xiao et al. (2018 1 . note that the current judicial research focuses more on classification tasks such as case outcome prediction, crime prediction, etc. it is difficult to carry out richer and multi-scenario tasks due to insufficient resources. our work fills this gap aiming to provide a comprehensive dataset for researchers to promote the progress of legal intelligence. ## experiments in this section, we describe the experiments conducted on ccd, and we introduce classical baseline models tested for the above-discussed tasks. ## conclusions we proposed a large-scale judicial dataset, court debate dataset (cdd) which contains real judicial debates and is annotated by experienced judges and students of law schools. cdd can be applied in academic research on a variety of downstream tasks, including fact finding, dialogue generation, feature recognition, element identification and role recognition. academic research results could be then put into practice in real-world applications leading to the interplay of theory and practice, and promoting the process of legal intelligence. in the future, we will continue to develop new models based on the provided dataset to improve results across diverse sub-tasks.