On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited. |
| Approach: | They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models. |
| Outcome: | The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models. |
Similar Papers
Social Bias in Multilingual Language Models: A Survey (2025.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained multilingual models exhibit the same social bias as models processing English texts. |
| Approach: | They examine the literature on bias evaluation and mitigation approaches in multilingual and non-English contexts and identify gaps in the field. |
| Outcome: | The proposed models perform well on multilingual language understanding benchmarks and are consistent with the current literature. |
Gender Bias in Masked Language Models for Multiple Languages (2022.naacl-main)
Copied to clipboard
| Challenge: | Masked Language Models (MLMs) pre-trained by predicting masked tokens on large corpora have been used successfully in natural language processing tasks for a variety of languages. |
| Approach: | They propose to use English attribute word lists to evaluate bias in eight languages without manually annotating data. |
| Outcome: | The proposed model significantly correlates with the existing English datasets for gender bias. |
Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. |
| Approach: | They propose a standard domain adaptation model to reduce gender bias in multilingual contexts. |
| Outcome: | The proposed model reduces gender bias and improves on two text classification tasks with three fair-aware baselines. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
Copied to clipboard
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation (2022.acl-long)
Copied to clipboard
| Challenge: | grammatical gender languages are characterized by morphosyntactic chains of gender agreement marked on a variety of lexical items and parts-of-speech (POS). |
| Approach: | They propose to enrich the natural, gender-sensitive MuST-SHE corpus with two new linguistic annotation layers to explore gender bias. |
| Outcome: | The proposed models shed light on gender bias and its detection at several levels of granularity. |
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models (2022.findings-naacl)
Copied to clipboard
| Challenge: | Pretrained language models (PLMs) have been shown to encapsulate social biases, including those relating to gender and race. |
| Approach: | They propose a new bias measure based on Jensen–Shannon divergence that retains more information from the model output probabilities than other previously proposed bias measures. |
| Outcome: | The proposed measure outperforms CrowS-Pairs and other similar measures for non-English datasets. |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
Copied to clipboard
Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to mitigate gender bias in pre-trained language models are often evaluated on datasets that check the extent to which the model is gender-neutral in its predictions. |
| Approach: | They propose to use a manually curated dataset to measure gender bias and to measure useful gender knowledge. |
| Outcome: | The proposed dataset aims to quantify gender biases and to assess their impact on useful gender knowledge. |
Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
Copied to clipboard
| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
Examining Gender Bias in Languages with Grammatical Gender (D19-1)
Copied to clipboard
| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
| Outcome: | The proposed methods reduce gender bias while preserving the original embeddings. |