The DOI matches its Crossref record; 71 of the 74 references it cites were found in a registry or an index.
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention
Open accessAbstract
Natural Language Processing (NLP) models are found to exhibit discriminatory stereotypes across many social constructs, e.g.gender and race.In comparison to the progress made in reducing bias from static word embeddings, fairness in sentence-level text encoders received little consideration despite their wider applicability in contemporary NLP tasks.In this paper, we propose a debiasing method for pretrained text encoders that both reduces social stereotypes, and inflicts next to no semantic damage.Unlike previous studies that directly manipulate the embeddings, we suggest to dive deeper into the operation of these encoders, and pay more attention to the way they pay attention to different social groups.We find that stereotypes are also encoded in the attention layer.Then, we work on model debiasing by redistributing the attention scores of a text encoder such that it forgets any preference to historically advantaged groups, and attends to all social classes with the same intensity.Our experiments confirm that reducing bias from attention effectively mitigates it from the model's text representations.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“understanding that attention mechanisms play a key role in encoding bias informs attention-targeting mitigations such as Attanasio et al. (2022), Gaci et al. (2022), and Zayed et al. (2023a).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Gaci et al. (2022)
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222
Knowledge Distillation: A Survey
Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01453-z · Cited by 3,931
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887
What Does BERT Look at? An Analysis of BERT’s Attention
Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w19-4828 · Cited by 1,953
Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *
Dastin, J. (2022) · Ethics of Data and Analytics · doi:10.1201/9781003278290-44 · Cited by 1,165
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050 · cited 2×
“based: These methods employ text encoders in downstream NLP tasks (Blodgett et al., 2020) such”
BackgroundMethodAttention is not not Explanation
Wiegreffe, S., & Pinter, Y. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1002 · Cited by 966
Attention is not ExplanationPreprint
Jain, S., & Wallace, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10186 · Cited by 897
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
Zhang, Z., Robinson, D., & Tepper, J. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93417-4_48 · Cited by 676
Measuring and Mitigating Unintended Bias in Text Classification
Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P., & Mukherjee, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17745 · Cited by 657
StereoSet: Measuring stereotypical bias in pretrained language models
Nadeem, M., Bethke, A., & Reddy, S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.416 · Cited by 643
Social Bias Frames: Reasoning about Social and Power Implications of Language
Sap, M., Gabriel, S., Qin, L., Jurafsky, D., Smith, N. A., & Choi, Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.486 · Cited by 469
On Measuring Social Biases in Sentence Encoders
May, C., Wang, A., Bordia, S., Bowman, S. R., & Rudinger, R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1063 · Cited by 461 · cited 4×
“For example, Caliskan et al. (2017) and May et al. (2019) compared the cosine similarity between representations of groups and attributes, and found that groups have unequal similarities.”
InfluentialGonen, H., & Goldberg, Y. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1061 · Cited by 454
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
Nangia, N., Vania, C., Bhalerao, R., & Bowman, S. R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.154 · Cited by 446
Learning Gender-Neutral Word Embeddings
Zhao, J., Zhou, Y., Li, Z., Wang, W. W., & Chang, K.-W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1521 · Cited by 426
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Borkan, D., Dixon, L., Sorensen, J. S., Thain, N., & Vasserman, L. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3317593 · Cited by 419
Us and them: identifying cyber hate on Twitter across multiple protected characteristics
Burnap, P., & Williams, M. L. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0072-6 · Cited by 397
From Word To Sense Embeddings: A Survey on Vector Representations of Meaning
Camacho-Collados, J., & Pilehvar, M. T. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11259 · Cited by 393
Reducing Gender Bias in Abusive Language Detection
Park, J. H., Shin, J., & Fung, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1302 · Cited by 323 · cited 1×
“Admittedly, common social biases have also been shown to exist in hate speech detection models, for example in associating toxicity to frequently attacked groups (such as "muslim" or "gay") even if the text itself is not toxic (Dixon et al., 2018; Park et al., 2018).”
Adversarial Removal of Demographic Attributes from Text Data
Elazar, Y., & Goldberg, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1002 · Cited by 307
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
Ravfogel, S., Elazar, Y., Gonen, H., Twiton, M., & Goldberg, Y. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.647 · Cited by 287
Manzini, T., Chong, L. Y., Black, A. W., & Tsvetkov, Y. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1062 · Cited by 284
Velupillai, S. U., Suominen, H., Liakata, M., Roberts, A., Shah, A. D., Morley, K. I., Osborn, D., Hayes, J., Stewart, R. J., Downs, J. M., et al. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.10.005 · Cited by 261
Characterizing and Detecting Hateful Users on Twitter
Ribeiro, M. H., Calais, P., Santos, Y., Almeida, V. A. F., & Meira, W. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15057 · Cited by 260
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Schick, T., Udupa, S., & Schütze, H. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00434 · Cited by 227
Identifying and Reducing Gender Bias in Word-Level Language Models
Bordia, S., & Bowman, S. R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop · doi:10.18653/v1/n19-3002 · Cited by 219
Controllable Invariance through Adversarial Feature LearningPreprint
Xie, Q., Dai, Z., Du, Y., Hovy, E. H., & Neubig, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.11122 · Cited by 206
Learning to Deceive with Attention-Based Explanations
Pruthi, D., Gupta, M., Dhingra, B., Neubig, G., & Lipton, Z. C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.432 · Cited by 203
Bastings, J., & Filippova, K. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.14 · Cited by 202
Measuring and Reducing Gendered Correlations in Pre-trained Models
Webster, K., Wang, X., Tenney, I., Beutel, A., Pitler, E., Pavlick, E., Chen, J., Chi, E., & Petrov, S. (2020) · arXiv · doi:10.48550/arxiv.2010.06032 · Cited by 195
Towards Debiasing Sentence Representations
Liang, P. P., Li, I., Zheng, E., Lim, Y. C., Salakhutdinov, R., & Morency, L. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.488 · Cited by 188
Towards Robust and Privacy-preserving Text Representations
Li, Y., Baldwin, T., & Cohn, T. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2005 · Cited by 181
Improving Document Ranking with Dual Word Embeddings
Nalisnick, E., Mitra, B., Craswell, N., & Caruana, R. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2889361 · Cited by 163
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
Hoover, B., Strobelt, H., & Gehrmann, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.22 · Cited by 151
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets
Blodgett, S. L., Lopez, G., Olteanu, A., Sim, R., & Wallach, H. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.81 · Cited by 147 · cited 1×
“We acknowledge that intrinsic metrics have recently been criticized (GoldfarbTarrant et al., 2020; Aribandi et al., 2021; Blodgett et al., 2021).”
BackgroundTenney, I., Wexler, J., Bastings, J., Bolukbasi, T., Coenen, A., Gehrmann, S., Jiang, E., Pushkarna, M., Radebaugh, C., Reif, E., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.15 · Cited by 138 · cited 1×
“Several techniques have used attention to dissect and explain the inner functioning of text encoders (Vig, 2019b; Clark et al., 2019; Hoover et al., 2020; Tenney et al., 2020; Bastings and Filippova, 2020).”
BackgroundTrytten, D., Lowe, A. W., & Walden, S. E. (2012) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2012.tb00057.x · Cited by 136
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Meade, N., Poole-Dayan, E., & Reddy, S. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.132 · Cited by 135
Debiasing Pre-trained Contextualised Embeddings
Kaneko, M., & Bollegala, D. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.107 · Cited by 134
Gender-preserving Debiasing for Pre-trained Word Embeddings
Kaneko, M., & Bollegala, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1160 · Cited by 127
Towards Controllable Biases in Language Generation
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.291 · Cited by 123
Sustainable Modular Debiasing of Language Models
Lauscher, A., Lueken, T., & Glavaš, G. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.411 · Cited by 120
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
Iandola, F., Shaw, A., Krishna, R., & Keutzer, K. (2020) · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing · doi:10.18653/v1/2020.sustainlp-1.17 · Cited by 114
Investigating Gender Bias in BERT
Bhardwaj, R., Majumder, N., & Poria, S. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09881-2 · Cited by 112
Law and Word Order: NLP in Legal Tech
Dale, R. (2018) · Natural Language Engineering · doi:10.1017/s1351324918000475 · Cited by 96
FairFil: Contrastive Neural Debiasing Method for Pretrained Text EncodersPreprint
Cheng, P., Hao, W., Yuan, S., Si, S., & Carin, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06413 · Cited by 93
Visualizing Attention in Transformer-Based Language Representation ModelsPreprint
Vig, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02679 · Cited by 88
“asians Are Good At Math” Is Not a Compliment: Stem Success As a Threat To Personhood
Shah, N. (2019) · Harvard Educational Review · doi:10.17763/1943-5045-89.4.661 · Cited by 67
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
Qian, Y., Muaz, U., Zhang, B., & Hyun, J. W. (2019) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p19-2031 · Cited by 63
Entropy-based attention regularization frees unintended bias mitigation from lists
Attanasio, G., Nozza, D., Hovy, D., & Baralis, E. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.88 · Cited by 56
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings
Kumar, V., Bhotia, T. S., Kumar, V., & Chakraborty, T. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00327 · Cited by 39
Intrinsic bias metrics do not correlate with application bias
Goldfarb-Tarrant, S., Marchant, R., Muñoz Sánchez, R., Pandya, M., & Lopez, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.150 · Cited by 33
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations
liang, S. M., Dufter, P., & Schütze, H. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.446 · Cited by 33
Addressing Age-Related Bias in Sentiment Analysis
Díaz, M., Johnson, I. L., Lazar, A., Piper, A. M., & Gergle, D. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/852 · Cited by 32
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language ModelsPreprint
Meade, N., Poole-Dayan, E., & Reddy, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08527 · Cited by 26
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack
Wang, L., Yan, Y., He, K., Wu, Y., & Xu, W. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.293 · Cited by 19
Quantifying Social Biases in Contextual Word Representations
Kurita, K., Vyas, N., Pareek, A., Black, A. W., & Tsvetkov, Y. (2019) · Cited by 16
How Reliable are Model Diagnostics?
Aribandi, V., Tay, Y., & Metzler, D. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.155 · Cited by 15
On measuring and mitigating biased inferences of word embeddings
Dev, S., Li, T., Phillips, J. M., & Srikumar, V. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6267 · Cited by 14
Iterative adversarial removal of gender bias in pretrained word embeddings
Gaci, Y., Benatallah, B., Casati, F., & Benabdeslem, K. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507274 · Cited by 10
Self-citationMasked Language Models as Stereotype Detectors?
Gaci, Y., Benatallah, B., Casati, F., & Benabdeslem, K. (2022) · HAL · doi:10.48786/edbt.2022.26 · Cited by 3
Self-citation
Research works printed but not linked: 5
- Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. Man is to computer programmer as woman is to home- maker? debiasing word embeddings. Advances in neural information processing systems, 29:4349- 4357.
- C Hansen, M Tosik, G Goossen, C Li, L Bayeva, F Berbain, and M Rotaru. 2015. How to get the best word vectors for resume parsing. In SNN Adaptive Intelligence/Symposium: Machine Learning.
- Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis- Philippe Morency. 2020a. Towards debiasing sen- tence representations. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 5502-5515.
- Zachary C Lipton. 2018. The mythos of model inter- pretability: In machine learning, the concept of in- terpretability is both important and slippery. Queue, 16(3):31-57.
- Jesse Vig. 2019a. Bertviz: A tool for visualizing multi- head self-attention in the bert model. In Proceedings of the 2019 ICLR Workshop on Debugging Machine Learning Models.
Cited by
Sorted by citations
Bias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 1,245
ROUGE-SEM: Better evaluation of summarization using ROUGE combined with semantics
Zhang, M., Li, C., Wan, M., Zhang, X., & Zhao, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121364 · Cited by 61
Deng, C., Duan, Y., Jin, X., Chang, H., Tian, Y., Liu, H., Wang, Y., Gao, K., Zou, H. P., Jin, Y., et al. (2025) · AI and Ethics · doi:10.1007/s43681-025-00797-3 · Cited by 73
Debiasing large language models: research opportunities*
Yogarajan, V., Dobbie, G., & Keegan, T. T. (2024) · Journal of the Royal Society of New Zealand · doi:10.1080/03036758.2024.2398567 · Cited by 18
Exploring the occupational biases and stereotypes of Chinese large language models
Jiang, L., Zhu, G., Sun, J., Cao, J., & Wu, J. X. (2025) · Scientific Reports · doi:10.1038/s41598-025-03893-w · Cited by 9
Metadata
- Authors
- Gaci, Y., Benatallah, B., Casati, F., & Benabdeslem, K.
- Affiliations
- Lyon 1 Université (FR)
- UNSW Sydney (AU)
- ServiceNow (United States) (US)
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 9582-9602
- OpenAlex
- W4318046968
- Cited by
- 14
- References
- 74
- Citations by year
- 2025: 5 · 2024: 3 · 2023: 6
- FWCI
- 3.20
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Iris (University of Trento) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- bias mitigation, attentional bias, social stereotypes, gender bias, racial bias, fairness, text representation, transformer models
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
