Abstract
Nowadays the community-based question answering (cQA) sites become popular Web service, which have accumulated millions of questions and their associated answers over time. Thus, the answer selection component plays an important role in a cQA system, which ranks the relevant answers to the given question. With the development of this area, problems of noise prevalence and data sparsity become more tough. In our paper, we consider the task of answer selection from two aspects including deep semantic matching and user community metadata representation. We propose a novel dual attentive neural network framework (DANN) to embed question topics and user network structures for answer selection. The representation of questions and answers are first learned by convolutional neural networks (CNNs). Then the DANN learns interactions of questions and answers, which is guided via user network structures and semantic matching of question topics with double attention. We evaluate the performance of our method on the well-known question answering site Stack exchange. The experiments show that our framework outperforms other state-of-the-art solutions to the problem.
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
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Fang, H., Wu, F., Zhao, Z., Duan, X., Zhuang, Y., Ester, M.: Community-based question answering via heterogeneous social network learning. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)
Hoogeveen, D., Verspoor, K.M., Baldwin, T.: CQADupStack: a benchmark data set for community question-answering research. In: Proceedings of the 20th Australasian Document Computing Symposium, p. 3. ACM (2015)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, pp. 2042–2050 (2014)
Iyyer, M., Boyd-Graber, J.L., Claudino, L.M.B., Socher, R., Daumé III, H.: A neural network for factoid question answering over paragraphs. In: EMNLP, pp. 633–644 (2014)
Jiang, M., Cui, P., Chen, X., Wang, F., Zhu, W., Yang, S.: Social recommendation with cross-domain transferable knowledge. IEEE Trans. Knowl. Data Eng. 27(11), 3084–3097 (2015)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1188–1196 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Qiu, X., Huang, X.: Convolutional neural tensor network architecture for community-based question answering. In: IJCAI, pp. 1305–1311 (2015)
Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015)
Shen, Y., Rong, W., Sun, Z., Ouyang, Y., Xiong, Z.: Question/answer matching for CQA system via combining lexical and sequential information. In: AAAI, pp. 275–281 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)
Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: ACL, vol. 2, pp. 707–712 (2015)
Yin, W., Schütze, H., Xiang, B., Zhou, B.: ABCNN: attention-based convolutional neural network for modeling sentence pairs. arXiv preprint arXiv:1512.05193 (2015)
Zhang, X., Li, S., Sha, L., Wang, H.: Attentive interactive neural networks for answer selection in community question answering. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Zhao, Z., Lu, H., Zheng, V.W., Cai, D., He, X., Zhuang, Y.: Community-based question answering via asymmetric multi-faceted ranking network learning. In: AAAI, pp. 3532–3539 (2017)
Zhou, G., Liu, Y., Liu, F., Zeng, D., Zhao, J.: Improving question retrieval in community question answering using world knowledge. In: IJCAI, vol. 13, pp. 2239–2245 (2013)
Acknowledgment
This work is supported by NSFC under Grant No. 61532001 and No. 61370054. We thank the three anonymous reviewers for their valuable comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Liu, Z., Li, M., Bai, T., Yan, R., Zhang, Y. (2018). A Dual Attentive Neural Network Framework with Community Metadata for Answer Selection. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-73618-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73617-4
Online ISBN: 978-3-319-73618-1
eBook Packages: Computer ScienceComputer Science (R0)