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)


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.


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



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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© 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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