Identification of Rhetorical Roles of Sentences in Indian Legal Judgments
This work addresses a domain-specific challenge for legal professionals by improving tasks like summarization and search, though it is incremental as it applies existing neural methods to a new dataset.
The paper tackled the problem of automatically identifying rhetorical roles of sentences in Indian Supreme Court judgments, and found that deep neural models significantly outperformed baseline methods using handcrafted features.
Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on. The task is challenging since legal case documents are usually not well-structured, and these rhetorical roles may be subjective (as evident from variation of opinions between legal experts). In this paper, we address this task for judgments from the Supreme Court of India. We label sentences in 50 documents using multiple human annotators, and perform an extensive analysis of the human-assigned labels. We also attempt automatic identification of the rhetorical roles of sentences. While prior approaches towards this task used Conditional Random Fields over manually handcrafted features, we explore the use of deep neural models which do not require hand-crafting of features. Experiments show that neural models perform much better in this task than baseline methods which use handcrafted features.