Intent Classification in Question-Answering Using LSTM Architectures
This addresses a modular part of the complex QA problem in NLP/AI, but it is incremental as it applies an existing method to a specific task.
The paper tackles intent classification in question-answering by using an LSTM network, showing it can be approached effectively and efficiently within a basic prototype responder.
Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (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.