Abstract
Answer selection aims to find the most appropriate answer from a set of candidate answers, playing an increasingly important role in Community-based Question Answering. However, existing studies overlook the correlation among historical answers of users and simply summarize user contexts by concatenation or max-pooling when modeling user representations. In this paper, we propose a novel User Context-aware Attention Network (UCAN) for the answer selection task. Specifically, we apply the BERT model to encode representations of questions, answers, and user contexts. Then we use the CNN model to extract the n-gram features. Next, we model the user context as a graph and utilize the graph attention mechanism to capture the correlation among answers in the user context. We further use the Bi-LSTM to enhance the contextual representations. Finally, we adopt a multi-view attention mechanism to learn the context-based semantic representations. We conduct experiments on two widely used datasets, and the experimental results show that the UCAN outperforms all baselines.
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Chen, Q., Wang, J., Lan, X., Zheng, N.: Preference relationship-based CrossCMN scheme for answer ranking in community QA. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 81–90. IEEE (2019)
Deng, Y., et al.: Multi-task learning with multi-view attention for answer selection and knowledge base question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6318–6325 (2019)
Deng, Y., Xie, Y., Li, Y., Yang, M., Lam, W., Shen, Y.: Contextualized knowledge-aware attentive neural network: enhancing answer selection with knowledge. ACM Trans. Inf. Syst. (TOIS) 40(1), 1–33 (2021)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of naacL-HLT, pp. 4171–4186 (2019)
Gao, Z., Xia, X., Lo, D., Grundy, J.: Technical Q&A site answer recommendation via question boosting. ACM Trans. Softw. Eng. Methodol. (TOSEM) 30(1), 1–34 (2020)
Jing, F., Ren, H., Cheng, W., Wang, X., Zhang, Q.: Knowledge-enhanced attentive learning for answer selection in community question answering systems. Knowl.-Based Syst. 250, 109117 (2022)
Lyu, S., Ouyang, W., Wang, Y., Shen, H., Cheng, X.: What we vote for? Answer selection from user expertise view in community question answering. In: The World Wide Web Conference, pp. 1198–1209 (2019)
Nakov, P., et al.: SemEval-2017 task 3: community question answering. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 27–48. Association for Computational Linguistics (2017)
Nakov, P., Màrquez, L., Magdy, W., Moschitti, A., Glass, J., Randeree, B.: SemEval-2015 task 3: answer selection in community question answering. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 269–281. Association for Computational Linguistics (2015)
Omari, A., Carmel, D., Rokhlenko, O., Szpektor, I.: Novelty based ranking of human answers for community questions. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 215–224. SIGIR ’16, Association for Computing Machinery (2016)
Tran, Q.H., Tran, D.V., Vu, T., Le Nguyen, M., Pham, S.B.: JAIST: combining multiple features for answer selection in community question answering. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 215–219 (2015)
Tymoshenko, K., Moschitti, A.: Assessing the impact of syntactic and semantic structures for answer passages reranking. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1451–1460 (2015)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lió, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=rJXMpikCZ
Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 707–712 (2015)
Wen, J., Ma, J., Feng, Y., Zhong, M.: Hybrid attentive answer selection in CQA with deep users modelling. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Wu, W., Sun, X., Wang, H.: Question condensing networks for answer selection in community question answering. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1746–1755 (2018)
Wu, W., Wang, H., Li, S.: Bi-directional gated memory networks for answer selection. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds.) CCL/NLP-NABD -2017. LNCS (LNAI), vol. 10565, pp. 251–262. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69005-6_21
Xiang, Y., et al.: Incorporating label dependency for answer quality tagging in community question answering via CNN-LSTM-CRF. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1231–1241 (2016)
Xie, Y., Shen, Y., Li, Y., Yang, M., Lei, K.: Attentive user-engaged adversarial neural network for community question answering. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp. 9322–9329 (2020)
Xu, Z., Zheng, H.T., Zhai, S., Wang, D.: Knowledge and cross-pair pattern guided semantic matching for question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 9370–9377 (2020)
Yang, H., Zhao, X., Wang, Y., Li, M., Chen, W., Huang, W.: DGQAN: dual graph question-answer attention networks for answer selection. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1230–1239 (2022)
Yang, H., et al.: BERTDAN: question-answer dual attention fusion networks with pre-trained models for answer selection. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13110, pp. 520–531. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92238-2_43
Yang, M., Chen, L., Lyu, Z., Liu, J., Shen, Y., Wu, Q.: Hierarchical fusion of common sense knowledge and classifier decisions for answer selection in community question answering. Neural Netw. 132, 53–65 (2020)
Yih, S.W.T., Chang, M.W., Meek, C., Pastusiak, A.: Question answering using enhanced lexical semantic models. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (2013)
Zhang, W., Chen, Z., Dong, C., Wang, W., Zha, H., Wang, J.: Graph-based tri-attention network for answer ranking in CQA. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 14463–14471 (2021)
Zhao, C., Xu, L., Huang, H.: Exploiting user activities for answer ranking in Q &A forums. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds.) CollaborateCom 2017. LNICST, vol. 252, pp. 693–703. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00916-8_63
Acknowledgments
This work is partially supported by National Natural Science Foundation of China Nos. U1811263, 62072349, National Key Research and Development Project of China No. 2020YFC1522602.
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He, Y., Zhang, J., Yang, X., Peng, Z. (2023). User Context-Aware Attention Networks for Answer Selection. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_6
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