Abstract
In light of recent studies that show neural retrieval methods may intensify gender biases during retrieval, the objective of this paper is to propose a simple yet effective sampling strategy for training neural rankers that would allow the rankers to maintain their retrieval effectiveness while reducing gender biases. Our work proposes to consider the degrees of gender bias when sampling documents to be used for training neural rankers. We report our findings on the MS MARCO collection and based on different query datasets released for this purpose in the literature. Our results show that the proposed light-weight strategy can show competitive (or even better) performance compared to the state-of-the-art neural architectures specifically designed to reduce gender biases.
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References
Baeza-Yates, R.: Bias on the web. Commun. ACM 61(6), 54–61 (2018)
Baeza-Yates, R.: Bias in search and recommender systems. In: Fourteenth ACM Conference on Recommender Systems, pp. 2–2 (2020)
Bigdeli, A., Arabzadeh, N., Seyersalehi, S., Zihayat, M., Bagheri, E.: On the orthogonality of bias and utility in ad hoc retrieval. In: Proceedings of the 44rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)
Bigdeli, A., Arabzadeh, N., Zihayat, M., Bagheri, E.: Exploring gender biases in information retrieval relevance judgement datasets. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 216–224. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72240-1_18
Caliskan, A., Bryson, J.J., Narayanan, A.: Semantics derived automatically from language corpora contain human-like biases. Science 356(6334), 183–186 (2017)
Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: Electra: pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Fabris, A., Purpura, A., Silvello, G., Susto, G.A.: Gender stereotype reinforcement: measuring the gender bias conveyed by ranking algorithms. Inf. Process. Manage. 57(6), 102–377 (2020)
Font, J.E., Costa-Jussa, M.R.: Equalizing gender biases in neural machine translation with word embeddings techniques. arXiv preprint arXiv:1901.03116 (2019)
Gao, L., Callan, J.: Unsupervised corpus aware language model pre-training for dense passage retrieval. arXiv preprint arXiv:2108.05540 (2021)
Gao, L., Dai, Z., Callan, J.: Coil: revisit exact lexical match in information retrieval with contextualized inverted list. arXiv preprint arXiv:2104.07186 (2021)
Gerritse, E.J., Hasibi, F., de Vries, A.P.: Bias in conversational search: the double-edged sword of the personalized knowledge graph. In: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval, pp. 133–136 (2020)
Han, S., Wang, X., Bendersky, M., Najork, M.: Learning-to-rank with BERT in TF-ranking. arXiv preprint arXiv:2004.08476 (2020)
Izacard, G., Grave, E.: Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282 (2020)
Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., Yih, W.T.: Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906 (2020)
Macdonald, C., Tonellotto, N.: On approximate nearest neighbour selection for multi-stage dense retrieval. arXiv preprint arXiv:2108.11480 (2021)
Nguyen, T., et al.: Ms marco: a human generated machine reading comprehension dataset. In: CoCo@ NIPS (2016)
Nogueira, R., Cho, K.: Passage re-ranking with BERT. arXiv preprint arXiv:1901.04085 (2019)
Olteanu, A., et al.: Facts-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. In: ACM SIGIR Forum, vol. 53, pp. 20–43. ACM New York, NY, USA (2021)
Pradeep, R., Nogueira, R., Lin, J.: The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models. arXiv preprint arXiv:2101.05667 (2021)
Qu, Y., et al.: RocketQA: an optimized training approach to dense passage retrieval for open-domain question answering. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5835–5847 (2021)
Rekabsaz, N., Kopeinik, S., Schedl, M.: Societal biases in retrieved contents: measurement framework and adversarial mitigation for BERT rankers. arXiv preprint arXiv:2104.13640 (2021)
Rekabsaz, N., Schedl, M.: Do neural ranking models intensify gender bias? In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2065–2068 (2020)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)
Sun, T., et al.: Mitigating gender bias in natural language processing: literature review. arXiv preprint arXiv:1906.08976 (2019)
Turc, I., Chang, M.W., Lee, K., Toutanova, K.: Well-read students learn better: on the importance of pre-training compact models. arXiv preprint arXiv:1908.08962 (2019)
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Bigdeli, A., Arabzadeh, N., Seyedsalehi, S., Zihayat, M., Bagheri, E. (2022). A Light-Weight Strategy for Restraining Gender Biases in Neural Rankers. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_6
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