Abstract
Generative question answering systems aim at generating more contentful responses and more natural answers. Existing generative question answering systems applied to knowledge grounded conversation generate natural answers either with a knowledge base or with raw text. Nevertheless, performance of their methods is often affected by the incompleteness of the KB or text facts. In this paper, we propose an end-to-end generative question answering model. We make use of unstructured text and structured KBs to establish an universal schema as a large external facts library. Each words of a natural answer are dynamically predicted from the common vocabulary and retrieved from the corresponding external facts. And our model can generate natural answer containing arbitrary number of knowledge entities through selecting from multiple relevant external facts by the dynamic knowledge enquirer. Finally, empirical study shows that our model is efficient and outperforms baseline methods significantly in terms of automatic evaluation and human evaluation.
Keywords
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Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comput. Sci. (2014)
Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. Eprint Arxiv (2014)
Das, R., Zaheer, M., Reddy, S., Mccallum, A.: Question answering on knowledge bases and text using universal schema and memory networks, pp. 358–365 (2017)
Ghazvininejad, M., et al.: A knowledge-grounded neural conversation model (2017)
Han, S., Bang, J., Ryu, S., Lee, G.G.: Exploiting knowledge base to generate responses for natural language dialog listening agents. In: Meeting of the Special Interest Group on Discourse and Dialogue, pp. 129–133 (2015)
He, S., et al.: Generating natural answers by incorporating copying and retrieving mechanisms in sequence-to-sequence learning. In: Meeting of the Association for Computational Linguistics, pp. 199–208 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Miller, A., Fisch, A., Dodge, J., Karimi, A.H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents, pp. 1400–1409 (2016)
Papineni, S.: Blue: a method for automatic evaluation of machine translation. In: Meeting of the Association for Computational Linguistics (2002)
Riedel, S., Yao, L., Mccallum, A., Marlin, B.M.: Relation extraction with matrix factorization and universal schemas. In: NAACL-HLT, pp. xxi–xxii (2013)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Press (1997)
Serban, I.V., Sordoni, A., Bengio, Y., Courville, A., Pineau, J.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 3776–3783 (2016)
Serban, I.V., et al.: A hierarchical latent variable encoder-decoder model for generating dialogues (2016)
Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation, pp. 52–58 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks, vol. 4, pp. 3104–3112 (2014)
Yao, K., Zweig, G., Peng, B.: Attention with intention for a neural network conversation model. Comput. Sci. (2015)
Yao, L., Riedel, S., Mccallum, A.: Collective cross-document relation extraction without labelled data. University of Massachusetts, Amherst (2010)
Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., Li, X.: Neural generative question answering, vol. 27, pp. 2972–2978 (2015)
Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Conference on Empirical Methods in Natural Language Processing, pp. 1753–1762 (2015)
Zhu, W., Mo, K., Zhang, Y., Zhu, Z., Peng, X., Yang, Q.: Flexible end-to-end dialogue system for knowledge grounded conversation (2017)
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Ye, Z., Cai, R., Liao, Z., Hao, Z., Li, J. (2018). Generating Natural Answers on Knowledge Bases and Text by Sequence-to-Sequence Learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_44
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DOI: https://doi.org/10.1007/978-3-030-01418-6_44
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