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.
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References
Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: EMNLP, vol. 2, p. 6 (2013)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)
Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 (2014)
Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Bordes, A., Weston, J., Usunier, N.: Open question answering with weakly supervised embedding models. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS, vol. 8724, pp. 165–180. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44848-9_11
Cai, Q., Yates, A.: Large-scale semantic parsing via schema matching and lexicon extension. In: ACL, vol. 1, pp. 423–433 (2013)
Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over freebase with multi-column convolutional neural networks. In: ACL, vol. 1, pp. 260–269 (2015)
Golub, D., He, X.: Character-level question answering with attention. arXiv preprint arXiv:1604.00727 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kwiatkowski, T., Choi, E., Artzi, Y., Zettlemoyer, L.: Scaling semantic parsers with on-the-fly ontology matching. In: Proceedings of EMNLP. Citeseer, Percy (2013)
Serban, I.V., GarcÃa-Durán, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., Bengio, Y.: Generating factoid questions with recurrent neural networks: the 30m factoid question-answer corpus. arXiv preprint arXiv:1603.06807 (2016)
Yao, X., Van Durme, B.: Information extraction over structured data: Question answering with freebase. In: ACL, vol. 1, pp. 956–966. Citeseer (2014)
Yih, S.W.t., Chang, M.W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base (2015)
Zettlemoyer, L.S., Collins, M.: Learning context-dependent mappings from sentences to logical form. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and The 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, pp. 976–984. Association for Computational Linguistics (2009)
Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. arXiv preprint arXiv:1207.1420 (2012)
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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Wang, RZ., Zhan, CD., Ling, ZH. (2017). Question Answering with Character-Level LSTM Encoders and Model-Based Data Augmentation. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_25
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