End to End Long Short Term Memory Networks for Non-Factoid Question Answering

Retrieving correct answers for non-factoid queries poses significant challenges for current answer retrieval methods. Methods either involve the laborious task of extracting numerous features or are ineffective for longer answers. We approach the task of non-factoid question answering using deep learning methods without the need of feature extraction. Neural networks are capable of learning complex relations based on relatively simple features which make them a prime candidate for relating non-factoid questions to their answers. In this paper, we show that end to end training with a Bidirectional Long Short Term Memory (BLSTM) network with a rank sensitive loss function results in significant performance improvements over previous approaches without the need for combining additional models.