| Challenge: | Gender bias in natural language processing (NLP) applications has been receiving increasing attention, largely due to the lack of datasets and resources. |
| Approach: | They propose a corpus for gender identification and rewriting in contexts involving one or two target users with independent grammatical gender preferences. |
| Outcome: | The proposed corpus expands on Habash et al.'s Arabic Parallel Gender Corpus (APGC) by adding second person targets and increasing the total number of sentences over 6.5 times, reaching over 590K words. |
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| Challenge: | Existing systems that embed and amplify gender bias can still exhibit and exacerbate this problem. |
| Approach: | They propose a multi-step system that combines the positive aspects of rule-based and neural rewriting models to provide personalized outputs based on the users’ grammatical gender preferences. |
| Outcome: | The proposed system achieves 88.42 M2 F0.5 on a blind test set and improves over previous work on the first-person-only version of this task by 3.05 absolute increase in M2F0.5. |
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus (2020.acl-main)
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| Challenge: | a growing number of studies have examined the issue of gender bias in speech translation . a gender bias is a systemic problem that reproduces gender stereotypes discriminating women. |
| Approach: | They present the first thorough investigation of gender bias in speech translation . they compare audio technologies for English-Italian/French translations . |
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MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation (2026.acl-long)
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| Challenge: | In morphologically rich languages, gender influences verb conjugation, pronouns, and even first-person constructions with explicit and implicit mentions of gender. |
| Approach: | They propose a morphologically grounded large-scale benchmark dataset for evaluating gender-aware generation in three typologically diverse grammatically gendered languages: French, Arabic, and Hindi. |
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GeNRe: A French Gender-Neutral Rewriting System Using Collective Nouns (2025.findings-acl)
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| Challenge: | Gender rewriting is an NLP task that uses gendered forms to mitigate gender biases. |
| Approach: | They propose a French gender-neutral rewriting system using collective nouns, which are gender-fixed in French. |
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Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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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 . |
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Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation (2022.acl-long)
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| 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. |
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Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model (2023.acl-long)
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| Challenge: | Existing work has explored using sequence-to-sequence rewriting models to transform biased outputs into more gender-fair language by creating pseudo training data through linguistic rules. |
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Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| 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. |
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GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation (2021.emnlp-main)
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| Challenge: | Recent studies have focused on gender bias in neural machine translation (NMT) incorrectly gendered translations can reflect or amplify social biases. |
| Approach: | They propose to use a monolingual corpus to generate gender-specific pseudo-parallel corpora and filter them to improve gender translation accuracy. |
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Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)
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| Challenge: | Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets. |
| Approach: | They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments. |
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