The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Neural Ranking Models with Weak Supervision
Open accessAbstract
Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection (Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models.
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Cited by
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search
Dai, Z., Xiong, C., Callan, J., & Liu, Z. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159659 · Cited by 341
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
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Distilling Knowledge from Reader to Retriever for Question Answering
Izacard, G., & Grave, E. (2020) · arXiv · doi:10.48550/arxiv.2012.04584 · Cited by 102
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A reinforcement learning framework for relevance feedback
Montazeralghaem, A., Zamani, H., & Allan, J. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401099 · Cited by 36
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Metadata
- Authors
- Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B.
- Venue
- Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2017-08-07
- Pages
- 65-74
- OpenAlex
- W2610935556
- Cited by
- 299
- References
- 39
- FWCI
- 35.37
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Dehghani, M., Zamani, H., Severyn, A., Kamps, J., & Croft, W. B. (2017). Neural Ranking Models with Weak Supervision. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3077136.3080832
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