Extensive study on the underlying gender bias in contextualized word embeddings

Basta, Christine; Costa-jussà, Marta R.; Casas, Noe

doi:10.1007/s00521-020-05211-z

Cited by 14 publications

(11 citation statements)

References 26 publications

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“…The relative success of compression over CEAT may be because the compression rate was calculated on the same dataset as the extrinsic metrics, whereas CEAT was measured on a different dataset not necessarily aligned with a specific downstream task. The use of a non-task-aligned dataset is a common strategy among other intrinsic metrics (May et al, 2019;Kurita et al, 2019;Basta et al, 2021). Another possible explanation is that compression rate measures a more focused concept, namely the gender information within the internal representations.…”

Section: Discussionmentioning

confidence: 99%

How Gender Debiasing Affects Internal Model Representations, and Why It Matters

Orgad¹,

Goldfarb-Tarrant²,

Belinkov³

2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representations. However, the relationship between extrinsic and intrinsic bias is relatively unknown. In this work, we illuminate this relationship by measuring both quantities together: we debias a model during downstream fine-tuning, which reduces extrinsic bias, and measure the effect on intrinsic bias, which is operationalized as bias extractability with information-theoretic probing. Through experiments on two tasks and multiple bias metrics, we show that our intrinsic bias metric is a better indicator of debiasing than (a contextual adaptation of) the standard WEAT metric, and can also expose cases of superficial debiasing. Our framework provides a comprehensive perspective on bias in NLP models, which can be applied to deploy NLP systems in a more informed manner. 1 * Supported by the Viterbi Fellowship in the Center for Computer Engineering at the Technion.

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Section: Discussionmentioning

confidence: 99%

How Gender Debiasing Affects Internal Model Representations, and Why It Matters

Orgad¹,

Goldfarb-Tarrant²,

Belinkov³

2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…Methods of removing bias tend to be mathematically focused, such as Basta et al (2020) and Borkan et al (2019). As McCradden et al (2020) state, typical ML bias mitigation approaches assume biases' harms can be mathematically represented, though no evidence of the relevance of proposed bias metrics to the real world exists.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text

Havens¹,

Alex²,

Bach³

et al. 2022

Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

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Mitigating harms from gender biased language in Natural Language Processing (NLP) systems remains a challenge, and the situated nature of language means bias is inescapable in NLP data. Though efforts to mitigate gender bias in NLP are numerous, they often vaguely define gender and bias, only consider two genders, and do not incorporate uncertainty into models. To address these limitations, in this paper we present a taxonomy of gender biased language and apply it to create annotated datasets. We created the taxonomy and annotated data with the aim of making gender bias in language transparent. If biases are communicated clearly, varieties of biased language can be better identified and measured. Our taxonomy contains eleven types of gender biases inclusive of people whose gender expressions do not fit into the binary conceptions of woman and man, and whose gender differs from that they were assigned at birth, while also allowing annotators to document unknown gender information. The taxonomy and annotated data will, in future work, underpin analysis and more equitable language model development.

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“…Within the field of Machine Translation (MT), Vanmassenhove and Hardmeier (2018); Vanmassenhove et al (2019), andBasta et al (2020) in-corporate meta-information in the form of gender tags on the source side to enable gender alternative target translations for ambiguous source sentences. Moryossef et al (2019) propose a black-box approach by appending gender information to the target sentences using parataxis constructions at translation time.…”

Section: Related Workmentioning

confidence: 99%

NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives

Vanmassenhove

Emmery

Shterionov

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono-and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents. In this work, we present a rulebased and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets.

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Extensive study on the underlying gender bias in contextualized word embeddings

Cited by 14 publications

References 26 publications

How Gender Debiasing Affects Internal Model Representations, and Why It Matters

How Gender Debiasing Affects Internal Model Representations, and Why It Matters

Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text

NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives

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