The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion
Open accessAbstract
Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a novel hierarchical recurrent encoder-decoder architecture that makes possible to account for sequences of previous queries of arbitrary lengths. As a result, our suggestions are sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that our model outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our architecture is general enough to be used in a variety of other applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5590 · Cited by 1,005
Self-citation
Cited by
Hierarchical Multiscale Recurrent Neural Networks
Chung, J., Ahn, S., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.01704 · Cited by 386
Self-citationSummarizing Medical Conversations via Identifying Important Utterances
Song, Y., Tian, Y., Wang, N., & Xia, F. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.63 · Cited by 73
Lin, S.-C., Yang, J.-H., Nogueira, R., Tsai, M.-F., Wang, C.-J., & Lin, J. (2020) · arXiv · doi:10.48550/arxiv.2005.02230 · Cited by 62
Conversational Query Understanding Using Sequence to Sequence Modeling
Ren, G., Ni, X., Malik, M., & Ke, Q. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186083 · Cited by 55
Interactive Intent Modeling for Exploratory Search
Ruotsalo, T., Peltonen, J., Eugster, M. J. A., Głowacka, D., Floréen, P., Myllymäki, P., Jacucci, G., & Kaski, S. (2018) · ACM Transactions on Information Systems · doi:10.1145/3231593 · Cited by 57
User Modeling for Task Oriented Dialogues
Gur, I., Hakkani-Tur, D., Tur, G., & Shah, P. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639652 · Cited by 48
Learning towards conversational AI: A survey
Fu, T., Gao, S., Zhao, X., Wen, J.-R., & Yan, R. (2022) · AI Open · doi:10.1016/j.aiopen.2022.02.001 · Cited by 46
Capability-aware Prompt Reformulation Learning for Text-to-Image Generation
Zhan, J., Ai, Q., Liu, Y., Chen, J., & Ma, S. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657787 · Cited by 7
Metadata
- Authors
- Sordoni, A., Bengio, Y., Vahabi, H., Lioma, C., Grue Simonsen, J., & Nie, J.-Y.
- Venue
- Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2015-10-17
- Pages
- 553-562
- OpenAlex
- W1993378086
- Cited by
- 472
- References
- 5
- FWCI
- 35.08
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
