Abstract
Attention-based deep learning network models have shown obvious advantages on the sentence representation in many NLP tasks. While in the answer selection domain, applying attention-based deep learning model to capture complex semantic relations between question and answer is an extremely challenging task. In this paper, instead of simply using max-pooling in the pooling layer, we propose the two-level attentive pooling model which can efficiently select several key and high semantic-related matching words in the question-answer pair to improve the accuracy of answer selection. Specially, our model is built on top of the hybrid network which includes GRU and CNN to encode the complex sentence representation. The experimental evaluation on two popular datasets shows that our model has the good effectiveness and achieves the state-of-art performance in the answer selection task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXivpreprint arXiv:1409.0473, pp. 1–15 (2014)
Chung, J., Gulcehre, C., Cho, K.H., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, pp. 1–9 (2014)
Cui, Y., Chen, Z., Wei, S., et al.: Attention-over-attention neural networks for reading comprehension. arXiv preprint arXiv:1607.04423, pp. 1–10 (2016)
Zhang, J., Zhu, X., Chen, Q., et al.: Exploring question understanding and adaptation in neural-network-based question answering. arXiv preprint arXiv:1703.04617, pp. 1–11 (2017)
Yang, L., Ai, Q., Guo, J., et al.: ANMM: ranking short answer texts with attention-based neural matching model. In: 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, pp. 927–935. ACM (2016)
NLPCC2016-DBQA-DATA. http://pan.baidu.com/s/1c138KZQ. Accessed 21 May 2018
Hermann, K.M., Kočiský, T., Grefenstette, E., et al.: Teaching machines to read and comprehend. In: International Conference on Advances in Neural Information Processing Systems, Montréal, pp. 1693–1701. PMLR (2015)
Yoshida, M., Matsumoto, K., Kita, K.: Distributed representations for words on tables. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 135–146. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_11
Park, G., Lee, H., Kim, H.: Named entity recognition model based on neural networks using parts of speech probability and gazetteer features. Adv. Sci. Lett. 23(10), 9530–9533 (2017)
Feng, M., Xiang, B., Glass, M.R., et al.: Applying deep learning to answer selection: a study and an open task. In: International Conference on Automatic Speech Recognition and Understanding, Scottsdale, AZ, pp. 813–820. IEEE (2016)
Santos, C., Tan, M., Xiang, B., et al.: Attentive pooling networks. arXiv preprint arXiv:1602.03609, pp. 1–10 (2016)
Salamon, J., Bello, J.P.: Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Sig. Process. Lett. 24(3), 279–283 (2017)
Yang, Y., Wang, F., Zhang, J., Xu, Y., Philip, S.: A topic model for co-occurring normal documents and short texts. World Wide Web 21(2), 487–513 (2018)
Fu, J., Qiu, X., Huang, X.: Convolutional deep neural networks for document-based question answering. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 790–797. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_71
Tan, M., Santos, C.D., Xiang, B., Zhou, B.: LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108, pp. 1–11 (2015)
Acknowledgment
This work is partially supported by the National Natural Science Foundation of China (61772366), the Natural Science Foundation of Shanghai (17ZR1445900) and the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, Z., Shan, G., Cheng, J., Ni, J. (2018). A Two-Level Attentive Pooling Based Hybrid Network for Question Answer Matching Task. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-98812-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98811-5
Online ISBN: 978-3-319-98812-2
eBook Packages: Computer ScienceComputer Science (R0)