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Question Answering with Character-Level LSTM Encoders and Model-Based Data Augmentation

  • Run-Ze Wang
  • Chen-Di Zhan
  • Zhen-Hua LingEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)

Abstract

This paper presents a character-level encoder-decoder modeling method for question answering (QA) from large-scale knowledge bases (KB). This method improves the existing approach [9] from three aspects. First, long short-term memory (LSTM) structures are adopted to replace the convolutional neural networks (CNN) for encoding the candidate entities and predicates. Second, a new strategy of generating negative samples for model training is adopted. Third, a data augmentation strategy is applied to increase the size of the training set by generating factoid questions using another trained encoder-decoder model. Experimental results on the SimpleQuestions dataset and the Freebase5M KB demonstrates the effectiveness of the proposed method, which improves the state-of-the-art accuracy from 70.3% to 78.8% when augmenting the training set with 70,000 generated triple-question pairs.

Keywords

Question answering Knowledge base Long short-term memory Encoder-Decoder 

Notes

Acknowledgements

This paper was supported in part by the National Natural Science Foundation of China (Grants No. U1636201) and the Fundamental Research Funds for the Central Universities (Grant No. WK2350000001).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Engineering Laboratory for Speech and Language Information ProcessingUniversity of Science and Technology of ChinaHefeiChina

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