This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Riezler S, Liu Y. Query rewriting using monolingual statistical machine translation. Comput Linguist, 2010, 36: 569–582
Jones R, Rey B, Madani O, et al. Generating query substitutions. In: Proceedings of the 15th International Conference on World Wide Web, Edinburgh, 2006. 387–396
Gao J F, He X D, Xie S S, et al. Learning lexicon models from search logs for query expansion. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, 2013. 666–676
Song H J, Kim A, Park S B. Translation of natural language query into keyword query using a RNN encoderdecoder. In: Proceedings of International ACM SIGIR Conference, 2017. 965–968
Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 2015. ArXiv: 1409.0473v6
Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation. 2015. ArXiv: 1508.04025
Gu J, Lu Z, Li H, et al. Incorporating copying mechanism in sequence-to-sequence learning. 2016. ArXiv: 1603.06393
Riezler S, Liu Y, Vasserman A. Translating queries into snippets for improved query expansion. In: Proceedings of International Conference on Computational Linguistics, Manchester, 2008. 737–744
Rush A M, Chopra S, Weston J. A neural attention model for abstractive sentence summarization. 2015. ArXiv: 1509.00685
This work was partially supported by National Key Research and Development Plan (Grant No. 2017YFB1002104), National Natural Science Foundation of China (Grant Nos. 61532011, 61751201, 61473092, 61472088), and STCSM (Grant No. 16JC1420401, 17JC1420200). The authors would like to thank the anonymous reviewers for their helpful comments.
About this article
Cite this article
Liu, X., Pan, S., Zhang, Q. et al. Reformulating natural language queries using sequence-to-sequence models. Sci. China Inf. Sci. 62, 229103 (2019). https://doi.org/10.1007/s11432-018-9479-3