| Compared with traditional information retrieval systems,question answering systems provide a more natural way of human-machine interaction,allowing users to obtain information in a natural language.However,in many practical applications,the machine needs to proactively raise questions and understand the user's answer to determine the user's needs.This procedure is called reverse question answering(reverse QA).In almost all current solutions,the options are fixed,and human only allowed to select one by clicking buttons,which greatly harm the user's experience.To this end,this study proposed a novel deep network for answer understanding in reverse QA,which aims to provide users with a natural human-machine interaction method.Deep learning has achieved successes in natural language processing(NLP),and it is based on large-scale data.Therefore,this study explored a method for automatically generating data,that is,answer generation in reverse QA.In order to study the reverse QA conveniently,we constructed two data sets,namely TData and MData.This study focuses on the answer understanding and answer generation in reverse QA.For answer generation,the corresponding answer is generated by giving the question and the label.Feature-rich encoder is used to encode input information,and then decode it with attention mechanism.For answer understanding,this study fully models the correlation between questions and answers.The model including three modules: skeleton extraction for questions,relevance-aware representation of answers,and multi-hop based fusion.The goal of skeleton extraction for questions is mining important information in questions through unsupervised methods.Relevance-aware representation of answers is used to model the relationship of between questions and answers explicitly.Multi-hop based fusion is proposed for fusing the input information more fully.For these two problems,we verify the rationality and effectiveness of the proposed method on the constructed data set. |
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