Kim Mi-Young, Rabelo Juliano, Okeke Kingsley, Goebel Randy
Department of Science, Augustana Faculty, University of Alberta, Camrose, AB Canada.
Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB Canada.
Rev Socionetwork Strateg. 2022;16(1):157-174. doi: 10.1007/s12626-022-00103-1. Epub 2022 Feb 7.
We describe the techniques applied by the University of Alberta (UA) team in the most recent Competition on Legal Information Extraction and Entailment (COLIEE 2021). We participated in retrieval and entailment tasks for both case law and statute law; we applied a transformer-based approach for the case law entailment task, an information retrieval technique based on BM25 for legal information retrieval, and a natural language inference mechanism using semantic knowledge applied to statute law texts. This competition included 25 teams from 14 countries; our case law entailment approach was ranked no. 4 in Task 2, the BM25 technique for legal information retrieval was ranked no. 3 in Task 3, and the natural language inference technique incorporating semantic information was ranked no. 4 in Task 4. The combination of the latter two techniques on Task 5 was ranked no. 2. We also performed error analysis of our system in Task 4, which provides some insight into current state-of-the-art and research priorities for future directions.
我们描述了阿尔伯塔大学(UA)团队在最近的法律信息提取与蕴含竞赛(COLIEE 2021)中应用的技术。我们参与了判例法和成文法的检索与蕴含任务;我们将基于Transformer的方法应用于判例法蕴含任务,将基于BM25的信息检索技术应用于法律信息检索,并将使用语义知识的自然语言推理机制应用于成文法文本。本次竞赛有来自14个国家的25支队伍;我们的判例法蕴含方法在任务2中排名第4,用于法律信息检索的BM25技术在任务3中排名第3,结合语义信息的自然语言推理技术在任务4中排名第4。后两种技术在任务5中的组合排名第2。我们还对任务4中的系统进行了错误分析,这为当前的技术水平和未来研究方向的优先事项提供了一些见解。