Fine-grained Intent Classification in the Legal Domain

Ankan Mullick, Abhilash Nandy, Manav Nitin Kapadnis, Sohan Patnaik, Raghav R

Abstract:

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 finegrained 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