Abstract
This paper addresses the problem of answering a question by choosing the best answer from a set of candidate text fragments. This task requires to identify and measure the semantical relationship between the question and the candidate answers. Unlike previous solutions to this problem based on deep neural networks with million of parameters, we present a novel convolutional neural network approach that despite having a simple architecture is able to capture the semantical relationships between terms in a generated similarity matrix. The method was systematically evaluated over two different standard data sets. The results show that our approach is competitive with state-of-the-art methods despite having a simpler and efficient architecture.
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Notes
- 1.
GitHub passage retrieval code https://github.com/andresrosso/passage_retrieval.
References
Association for Computational Linguistics — ACL: Question Answering (State of the art) (2007)
Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive Presentation Sessions, pp. 69–72. Association for Computational Linguistics (2006)
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)
Etzioni, O.: Search needs a shake-up. Nature 476(7358), 25–26 (2011)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
He, H., Gimpel, K., Lin, J.J.: Multi-perspective sentence similarity modeling with convolutional neural networks. In: EMNLP, pp. 1576–1586 (2015)
Hua, H., Lin, J.: Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In: Proceedings of NAACL-HLT, pp. 937–948 (2016)
Heilman, M., Smith, N.A.: Tree edit models for recognizing textual entailments, paraphrases, and answers to questions. In: Human Language Technologies, pp. 1011–1019. Association for Computational Linguistics (ACL) (2010)
Hirschman, L., Gaizauskas, R.: Natural language question answering: the view from here. Nat. Lang. Eng. 7(04), 275–300 (2001)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, vol. 14, pp. 1188–1196 (2014)
Liu, F., Pennell, D., Liu, F., Liu, Y.: Unsupervised approaches for automatic keyword extraction using meeting transcripts. In: Proceedings of Human Language Technologies, pp. 620–628. Association for Computational Linguistics (ACL) (2009)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: word2vec (2014)
Rao, J., He, H., Lin, J.: Noise-contrastive estimation for answer selection with deep neural networks. In: Proceedings of the 25th ACM, pp. 1913–1916. ACM (2016)
Severyn, A., Moschitti, A.: Learning to rank short text pairs with convolutional deep neural networks. In: Proceedings ACM SIGIR Conference, pp. 373–382. ACM (2015)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Talathi, S.S., Vartak, A.: Improving performance of recurrent neural network with relu nonlinearity. CoRR, abs/1511.03771 (2015)
Wang, C., Kalyanpur, A., Boguraev, B.K.: Relation extraction and scoring in DeepQA. IBM J. Res. Dev. 56(3), 9:1–9:12 (2012)
Wang, M., Manning, C.D.: Probabilistic tree-edit models with structured latent variables for textual entailment and question answering. In: ACL Proceedings, pp. 1164–1172. Association for Computational Linguistics (2010)
Wang, M., Smith, N.A., Mitamura, T.: What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA, pp. 22–32, June 2007
Yang, L., Ai, Q., Guo, J., Croft, W.B.: aNNM: ranking short answer texts with attention-based neural matching model. In: Proceedings ACM, pp. 287–296. ACM (2016)
Yang, Y., Yih, W-T., Meek, C.: WikiQA: a challenge dataset for open-domain question answering. In: EMNLP, pp. 2013–2018. Citeseer (2015)
Yao, X., Van Durme, B., Callison-Burch, C., Clark, P.: Answer extraction as sequence tagging with tree edit distance. In: HLT-NAACL, pp. 858–867. Citeseer (2013)
Lei, Y., Hermann, K.M., Blunsom, P., Pulman, S.: Deep learning for answer sentence selection. In: NIPS Deep Learning Workshop (2014)
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Rosso-Mateus, A., González, F.A., Montes-y-Gómez, M. (2017). A Shallow Convolutional Neural Network Architecture for Open Domain Question Answering. In: Solano, A., Ordoñez, H. (eds) Advances in Computing. CCC 2017. Communications in Computer and Information Science, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-319-66562-7_35
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