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Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

  • Wei Li
  • Yunfang WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)

Abstract

In this paper, we focus on the problem of answer triggering addressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep interactions between the candidate answers and the question. Our result on F value achieves 42.6%, which surpasses the baseline by over 10 %.

Keywords

Answer Triggering Question Answering Hierarchical gated recurrent neural tensor network 

Notes

Acknowledgement

This work is supported by the National Key Basic Research Program of China (2014CB340504), the National Natural Science Foundation of China (61371129, 61572245).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Key Laboratory of Computational Linguistics (Peking University), Ministry of Education, School of Electronic Engineering and Computer SciencePeking UniversityBeijingChina

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