Full Text:   <172>

Summary:  <113>

CLC number: TP391

On-line Access: 2018-03-10

Received: 2017-11-25

Revision Accepted: 2018-01-24

Crosschecked: 2018-01-25

Cited: 0

Clicked: 589

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xin-yu Duan

Xin-yu DUAN, http://orcid.org/0000-0002-6803-7964

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.1 P.104-115

10.1631/FITEE.1700788


Temporality-enhanced knowledge memory network for factoid question answering


Author(s):  Xin-yu Duan, Si-liang Tang, Sheng-yu Zhang, Yin Zhang, Zhou Zhao, Jian-ru Xue, Yue-ting Zhuang, Fei Wu

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   duanxinyu@zju.edu.cn, siliang@zju.edu.cn, light.e.gal@gmail.com, zhangyin98@zju.edu.cn, zhaozhou@zju.edu.cn, jrxue@mail.xjtu.edu.cn, yzhuang@zju.edu.cn, wufei@zju.edu.cn

Key Words:  Question answering, Knowledge memory, Temporality interaction


Xin-yu Duan, Si-liang Tang, Sheng-yu Zhang, Yin Zhang, Zhou Zhao, Jian-ru Xue, Yue-ting Zhuang, Fei Wu. Temporality-enhanced knowledge memory network for factoid question answering[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(10): 104-115.

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Abstract: 
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.

时序增强的知识记忆网络在问答中的应用

概要:问答系统旨在为人类以自然语言提出的问题提供具体答案。如何对问题做出有效回答是该领域的热点问题。在问答系统研究中,对于给定问题与其相应答案,现有方法通常侧重于模拟问答语料间语义相关性或句法关系。当一个问题包含的答案线索非常少时,这些模型大多受到影响。本文设计了一个名为时序增强型知识记忆网络(temporality-enhanced knowledge memory network, TE-KMN)的架构,并将该模型应用于一个名为Quiz Bowl的知识竞赛问答数据集。与多数现有方法不同,该模型不仅对文本内容进行编码,也对问题中连续语句间能够逐步揭示答案的时序线索进行编码。此外,该模型通过协同使用外部知识,能够更好理解给定问题。实验结果表明,该方法性能优于目前几种最先进方法。

关键词:问答系统;知识记忆;时序增强

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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