Deep learning in question answering

Abstract

Question answering (QA) is a challenging task in natural language processing. Recently, with the remarkable success of deep learning on many natural language processing tasks, including semantic and syntactic analysis, machine translation, relation extraction, etc., more and more efforts have also been devoted to the task of question answering. This chapter briefly introduces the recent advances in deep learning methods on two typical and popular question answering tasks. (1) Deep learning in question answering over knowledge base (KBQA) which mainly employs deep neural networks to understand the meaning of the questions and try to translate them into structured queries, or directly translate them into distributional semantic representations compared with candidate answers in the knowledge base. (2) Deep learning in machine comprehension (MC) which manages to construct an end-to-end paradigm based on novel neural networks for directly computing the deep semantic matching among question, answers and the given passage.