Intent Classification in Question-Answering Using LSTM Architectures

Abstract: Question-answering (QA) is certainly the best known and probably also one of the most complex problem within NLP and AI. Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler parts. Assuming a modular approach to the problem, we confine our research to intent classification for an answer, given a question. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder.