# The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses arXiv: https://arxiv.org/abs/2110.09216 ยท HTML: https://mlsanity.com/paper/2110.09216/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 39 of its 59 references that are in the map. - First posted on arXiv 18 Oct 2021. ## Builds on (most cited first, 30 of 39) - [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018) - [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016) - [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020) - [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018) - [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019) - [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018) - [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019) - [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019) - [Evaluating Gender Bias in Machine Translation](https://mlsanity.com/paper/1906.00591/) (2019) - [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019) - [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018) - [Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology](https://mlsanity.com/paper/1906.04571/) (2019) - [Learning Gender-Neutral Word Embeddings](https://mlsanity.com/paper/1809.01496/) (2018) - [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://mlsanity.com/paper/1911.03842/) (2019) - [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://mlsanity.com/paper/1904.04047/) (2019) - [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021) - [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://mlsanity.com/paper/1909.00871/) (2019) - [Getting Gender Right in Neural Machine Translation](https://mlsanity.com/paper/1909.05088/) (2019) - [Does Gender Matter? Towards Fairness in Dialogue Systems](https://mlsanity.com/paper/1910.10486/) (2019) - [Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://mlsanity.com/paper/2004.04498/) (2020) - [Toward Gender-Inclusive Coreference Resolution](https://mlsanity.com/paper/1910.13913/) (2019) - [Multi-Dimensional Gender Bias Classification](https://mlsanity.com/paper/2005.00614/) (2020) - [Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques](https://mlsanity.com/paper/1901.03116/) (2019) - [Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00699/) (2020) - [Personalized Machine Translation: Preserving Original Author Traits](https://mlsanity.com/paper/1610.05461/) (2016) - [NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives](https://mlsanity.com/paper/2109.06105/) (2021) - [Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning](https://mlsanity.com/paper/2009.13028/) (2020) - [They, Them, Theirs: Rewriting with Gender-Neutral English](https://mlsanity.com/paper/2102.06788/) (2021) - [Mitigating Gender Bias in Machine Translation with Target Gender Annotations](https://mlsanity.com/paper/2010.06203/) (2020) - [Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It](https://mlsanity.com/paper/2010.05332/) (2020) ## Built on by (most cited first, 12 of 12) - [Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation](https://mlsanity.com/paper/2203.09866/) (2022) - [GATE: A Challenge Set for Gender-Ambiguous Translation Examples](https://mlsanity.com/paper/2303.03975/) (2023) - [What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study](https://mlsanity.com/paper/2410.00545/) (2024) - [User-Centric Gender Rewriting](https://mlsanity.com/paper/2205.02211/) (2022) - [MiTTenS: A Dataset for Evaluating Gender Mistranslation](https://mlsanity.com/paper/2401.06935/) (2024) - [ArabJobs: A Multinational Corpus of Arabic Job Ads](https://mlsanity.com/paper/2509.22589/) (2025) - [ArzEn-MultiGenre: An aligned parallel dataset of Egyptian Arabic song lyrics, novels, and subtitles, with English translations](https://mlsanity.com/paper/2508.01411/) (2025) - [The User-Aware Arabic Gender Rewriter](https://mlsanity.com/paper/2210.07538/) (2022) - [The Shared Task on Gender Rewriting](https://mlsanity.com/paper/2210.12410/) (2022) - [Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs](https://mlsanity.com/paper/2601.13099/) (2026) - [Towards Massive Multilingual Holistic Bias](https://mlsanity.com/paper/2407.00486/) (2024) - [MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation](https://mlsanity.com/paper/2604.18914/) (2026) ## Abstract (arXiv) Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems. Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts involving one or two target users (I and/or You) -- first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. The corpus has multiple parallel components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, as well as English, and English to Arabic machine translation output. This corpus expands on Habash et al. (2019)'s Arabic Parallel Gender Corpus (APGC v1.0) by adding second person targets as well as increasing the total number of sentences over 6.5 times, reaching over 590K words. Our new dataset will aid the research and development of gender identification, controlled text generation, and post-editing rewrite systems that could be used to personalize NLP applications and provide users with the correct outputs based on their grammatical gender preferences. We make the Arabic Parallel Gender Corpus (APGC v2.0) publicly available.