The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search
Abstract
This paper presents \textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search. Instead of exact matching query and document n-grams, \textttConv-KNRM uses Convolutional Neural Networks to represent n-grams of various lengths and soft matches them in a unified embedding space. The n-gram soft matches are then utilized by the kernel pooling and learning-to-rank layers to generate the final ranking score. \textttConv-KNRM can be learned end-to-end and fully optimized from user feedback. The learned model»s generalizability is investigated by testing how well it performs in a related domain with small amounts of training data. Experiments on English search logs, Chinese search logs, and TREC Web track tasks demonstrated consistent advantages of \textttConv-KNRM over prior neural IR methods and feature-based methods.
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Cited by
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687
Self-citationColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Khattab, O., & Zaharia, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401075 · Cited by 1,301
Nogueira, R., & Cho, K. (2019) · arXiv · doi:10.48550/arxiv.1901.04085 · Cited by 615
Understanding the Behaviors of BERT in Ranking
Qiao, Y., Xiong, C., Liu, Z., & Liu, Z. (2019) · arXiv · doi:10.48550/arxiv.1904.07531 · Cited by 201
Self-citationDo neural ranking models intensify gender bias?
Rekabsaz, N., & Schedl, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401280 · Cited by 64
Metadata
- Authors
- Dai, Z., Xiong, C., Callan, J., & Liu, Z.
- Venue
- Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2018-02-02
- Pages
- 126-134
- OpenAlex
- W2783640434
- Cited by
- 341
- References
- 31
- FWCI
- 39.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Dai, Z., Xiong, C., Callan, J., & Liu, Z. (2018). Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3159652.3159659
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