Cited by 190Ethics and Social Impacts of AITopic ModelingGender Studies in Language

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Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

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Basta, Christine, Costa-jussà, Marta R., Casas, Noe·2019·Proceedings of the First Workshop on Gender Bias in Natural Language Processing

DOI 10.18653/v1/w19-3805

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Abstract

Gender bias is highly impacting natural language processing applications.Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources.Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in.In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias.Our analysis includes different measures previously applied in the literature to standard word embeddings.Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.

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Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (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/n19-1423 · Cited by 54,325
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · cited 1×
“The clusters (for one particular experiment out of the 10 of them) are shown in Figure 2 after applying UMAP (McInnes et al., 2018; McInnes et al., 2018) to the contextualized embeddings.”
Method
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
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
Universal Language Model Fine-tuning for Text Classification
Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100

Show all 25 references

Research works printed but not linked: 2

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Cor- rado, and Jeff Dean. 2013. Distributed representa- tions of words and phrases and their composition- ality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Ad- vances in Neural Information Processing Systems 26, pages 3111-3119.
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word rep- resentations. In Proceedings of the 2018 Confer- ence of the North American Chapter of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies, Volume 1 (Long Papers), pages 2227-2237, New Orleans, Louisiana. Association for Computational Linguistics.

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Authors: Basta, C., Costa-jussà, M. R., & Casas, N.
Affiliations: Universitat Politècnica de Catalunya (ES)
ORCID: Basta, Christine
Venue: Proceedings of the First Workshop on Gender Bias in Natural Language Processing
Publisher: Association for Computational Linguistics
Published: 2019
Pages: 33-39
DOI: 10.18653/v1/w19-3805
OpenAlex: W2972668795
Cited by: 189
References: 25
Citations by year: 2026: 12 · 2025: 25 · 2024: 20 · 2023: 41 · 2022: 32 · 2021: 23 · 2020: 26 · 2019: 10
FWCI: 23.69
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Workshop on Gender Bias in Natural Language Processing (GeBNLP) (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, public-domain)
arXiv (Cornell University)
Funders: Agence Nationale de la Recherche (ANR-17-CHR2-0004)
Ministerio de Economía y Competitividad (TEC2015-69266-P)
Agència de Gestió d'Ajuts Universitaris i de Recerca
European Regional Development Fund (TEC2015-69266-P)
Agencia Estatal de Investigación
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Social Sciences › Gender Studies › Gender Studies in Language
Keywords: gender bias, contextual word embeddings, word embeddings

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Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

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