FAN Yixing1, 2, GUO Jiafeng1, LAN Yanyan1, XU Jun1, CHENG Xueqi1
1.CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2.University of Chinese Academy of Sciences, Beijing 100190, China
Abstract：Traditional researches on information retrieval are focuse on document-level retrieval, neglecting, sentence-level information retrieval which is of great importance in such applications, as searching in mobile phone Assuming that the context sentence could provide richer evidence for matching. this paper proposes a context-aware deep sentence matching model(CDSMM). Specifically, the model employs bi-directional LSTM to capture the interior and exterior information of the sentence; Then, a matching matrix is constructed based on the sentence representation and query representation; Finally, we get the matching score after a feed forward neural network. Experiment results on the WebAP dataset show that out model can significantly out-perform the state-of-the-art models.
[1] Shengxian Wan, Y Lan J. Guo, et al. A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, 2016. [2] Dolan W B, Brockett C. Automatically constructing a corpus of sentential paraphrases[C]//Proceedings of the 3rd International Workshop on Paraphrasing, Sydney, 2005. [3] Huang, Po-Sen, et al. Learning deep structured semantic models for web search using click through data[C]//Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management. San Francisco, 2013. [4] M. Surdeanu, M. Ciaramita, and H. Zaragoza. Learning to Rank Answers on Large Online QA Collections[C]//Proceedings of the 46th Annual Meeting of the ACL. Columbus, 2008:719-727. [5] P Molino, L M Aiello. Distributed Representations for Semantic Matching in Non-factoid Question Answering[C]//Proceedings of the 37th Annual ACM SIGIR Conference . Queensland, 2014:38-45. [6] P Jansen, M Surdeanu, P Clark. Discourse Complements Lexical Semantics for Non-factoid Answering Reranking[C]//Proceedings of the 52nd Annual Meeting of the ACL. Baltimore , 2014:977-986. [7] W. tau Yih, M W Chang, C Meek, et al. Question answering using enhanced lexical semantic models[C]//Proceedings of the 51st Annual Meeting of the ACL. Sofia, 2013. [8] X Yao, B V Durme, C Callison-burch, et al. Answer Extraction as Sequence Tagging With Tree Edit Distance[C]//Proceedings of the 11th Conference of the North American Chapter of the Association for Computational Linguistics. Atlanta, 2013. [9] Yu Lei, Karl Moritz Hermann, Phil Blunsom, et al. Deep learning for answer sentence selection[J]. Computer Science, arXiv preprint arXiv:1412. 1632, 2014. [10] M Bendersky, O Kurland. Utilizing passage-based language models for document retrieval[C]//Proceedings of the 30th European Conference on Information Retrieval, Glasgow, 2008:162-174. [11] X Liu, W Croft. Passage Retrieval Based on Language Models[C]//Proceedings of the 11th ACM Conference on Information and Knowledge Management. McLean, 2002:375-382. [12] Lv Yuanhua, Chengxiang Zhai. Positional language models for information retrieval[C]//Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. Boston, 2009. [13] Yang Liu, et al. Beyond Factoid QA:Effective Methods for Non-factoid Answer Sentence Retrieval[M]. European Conference on Information Retrieval. Berlin:Springer , 2016. [14] M Keikha, J H Park, W B Croft. Evaluating Answer Passages Using Summarization Measures[C]//Proceedings of the 37th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval. Gold Coast, 2014. [15] M KeikHa, J H Park, W B Croft, et al. Retrieving Passages and Finding Answers[C]//Proceedings of the 19th Australasian Document Computing Symposium. Melbourne, 2014:81-84. [16] Qiu Xipeng, Xuanjing Huang. Convolutional neural tensor network architecture for community-based question answering[C]//Proceedings of the 24th International Joint Conference on Artificial Intelligence. Buenos Aires, 2015. [17] Duchi John, Elad Hazan, Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization[J]. Journal of Machine Learning Research 2011, 12:2121-2159. [18] Omas Mikolov, Ilya Sutskever, Kai Chen, et al. Distributed Representations of Words and Phrases and their Compositionality[C]//Proceedings of the 27th Neural Information Processing Systems. Lake Tahoe, 2013. [19] S Huston, W B Croft. A comparison of Retrieval Models Using Term Dependences[C]//Proceedings of the 25th ACM Conference on Information and Knowledge Management. Shanghai, 2014. [20] D Metzler, W B Croft. A Markov Random Field Model for Term Dependencies[C]//Proceedings of 28th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval. Salvador, 2005. [21] C Zhai, J Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval[C]//Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New Orleans, 2001:334-342.
2017, Vol. 31 
