Fine-grained Intent Classification in the Legal Domain
This work addresses the need for automated intent analysis in legal documents to aid law practitioners, but it is incremental as it applies existing methods to a new domain-specific dataset.
The authors tackled the problem of understanding intent in legal documents by creating a dataset of 93 annotated legal cases across four categories and analyzing transformer-based models for intent extraction and classification, finding the dataset particularly challenging for fine-grained intent classification.
A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is essential that the parts of the document that express an intent corresponding to the case be clearly understood. In this paper, we introduce a dataset of 93 legal documents, belonging to the case categories of either Murder, Land Dispute, Robbery, or Corruption, where phrases expressing intent same as the category of the document are annotated. Also, we annotate fine-grained intents for each such phrase to enable a deeper understanding of the case for a reader. Finally, we analyze the performance of several transformer-based models in automating the process of extracting intent phrases (both at a coarse and a fine-grained level), and classifying a document into one of the possible 4 categories, and observe that, our dataset is challenging, especially in the case of fine-grained intent classification.