Obligation and Prohibition Extraction Using Hierarchical RNNs
This work addresses a domain-specific problem in legal document analysis, offering incremental improvements over existing methods.
The paper tackles the problem of detecting contractual obligations and prohibitions by introducing a hierarchical BILSTM model that processes sentence embeddings for classification. The hierarchical BILSTM outperforms the previous state-of-the-art BILSTM classifier with self-attention, achieving faster training and better performance due to its broader discourse view.
We consider the task of detecting contractual obligations and prohibitions. We show that a self-attention mechanism improves the performance of a BILSTM classifier, the previous state of the art for this task, by allowing it to focus on indicative tokens. We also introduce a hierarchical BILSTM, which converts each sentence to an embedding, and processes the sentence embeddings to classify each sentence. Apart from being faster to train, the hierarchical BILSTM outperforms the flat one, even when the latter considers surrounding sentences, because the hierarchical model has a broader discourse view.