The Language Model for Legal Retrieval and Bert based Model for Rhetorical Role Labeling for Legal Judgments
This paper mainly introduces the solutions to the two tasks published in FIRE2020(forum for information retrieval evaluation) For Task1 (statistic retrieval) The task 1 is, for a given query(description of a situation) identify relevant statutes and prior cases. This task includes two subtasks, Task1a (identifying relevant prior cases) and Task1b (identifying relevant statistics) For these two subtasks, we use the language model to score each query, and then rank them according to the score. For Task2(rhetorical role labeling for legal judgments) It requires us to classify sentences. We think it's a multi classification problem, and finally, we use Bert to complete the classification task. In the final result, the score of Task1a is 0.125, the score of Task1b is 0.2003, and the accuracy of Task2 is 0.549. The results and experiments show that the language model is a better way to complete Task1 and Bert is better to complete task2.
FIRE