Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has becom...Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has become an active line of research.Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer.Most of these models suffer when a question contains very little content that is indicative of the answer.In this paper,we devise an architecture named the temporality-enhanced knowledge memory network(TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl.Unlike most of the existing approaches,our model encodes not only the content of questions and answers,but also the temporal cues in a sequence of ordered sentences which gradually remark the answer.Moreover,our model collaboratively uses external knowledge for a better understanding of a given question.The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.展开更多
基金supported by the National Basic Research Program(973)of China(No.2015CB352302)the National Natural Science Foundation of China(Nos.61625107,U1611461,U1509206,and 61402403)+2 种基金the Key Program of Zhejiang Province,China(No.2015C01027)the Chinese Knowledge Center for Engineering Sciences and Technologythe Fundamental Research Funds for the Central Universities,China
文摘Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has become an active line of research.Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer.Most of these models suffer when a question contains very little content that is indicative of the answer.In this paper,we devise an architecture named the temporality-enhanced knowledge memory network(TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl.Unlike most of the existing approaches,our model encodes not only the content of questions and answers,but also the temporal cues in a sequence of ordered sentences which gradually remark the answer.Moreover,our model collaboratively uses external knowledge for a better understanding of a given question.The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.
