---
title: "The Shared Task on Gender Rewriting — Alhafni 2022"
description: "This paper reports on a shared task, held at WANLP 2022, on gender rewriting for Modern Standard… 36 references, cited by 2 papers here."
authors: ["Bashar Alhafni", "Nizar Habash", "Houda Bouamor", "Ossama Obeid", "Sultan Alrowili", "Daliyah Alzeer", "Khawlah M. Alshanqiti", "Ahmed ElBakry", "Muhammad ElNokrashy", "Mohamed Gabr", "Abderrahmane Issam", "Abdelrahim Qaddoumi", "K. Vijay-Shanker", "Mahmoud Zyate"]
year: 2022
venue: "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)"
source: "https://aclanthology.org/2022.wanlp-1.10/"
doi: "10.18653/v1/2022.wanlp-1.10"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/alhafni-2022-shared-task-gender-rewriting/"
---
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# The Shared Task on Gender Rewriting

**Authors:** [Bashar Alhafni](https://md.research.elamur.ai/free/en/authors/bashar-alhafni/), [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/), [Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/), [Ossama Obeid](https://md.research.elamur.ai/free/en/authors/ossama-obeid/), [Sultan Alrowili](https://md.research.elamur.ai/free/en/authors/sultan-alrowili/), [Daliyah Alzeer](https://md.research.elamur.ai/free/en/authors/daliyah-alzeer/), Khawlah M. Alshanqiti, [Ahmed ElBakry](https://md.research.elamur.ai/free/en/authors/ahmed-elbakry/), [Muhammad ElNokrashy](https://md.research.elamur.ai/free/en/authors/muhammad-elnokrashy/), [Mohamed Gabr](https://md.research.elamur.ai/free/en/authors/mohamed-gabr/), [Abderrahmane Issam](https://md.research.elamur.ai/free/en/authors/abderrahmane-issam/), [Abdelrahim Qaddoumi](https://md.research.elamur.ai/free/en/authors/abdelrahim-qaddoumi/), [K. Vijay-Shanker](https://md.research.elamur.ai/free/en/authors/k-vijay-shanker/) and Mahmoud Zyate · **Year:** 2022 · **Venue:** [Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2022](https://md.research.elamur.ai/free/en/venues/wanlp/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.wanlp-1.10/) · [PDF](https://aclanthology.org/2022.wanlp-1.10.pdf) · [DOI 10.18653/v1/2022.wanlp-1.10](https://doi.org/10.18653/v1/2022.wanlp-1.10) · [OpenAlex](https://openalex.org/W4385567119)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/alhafni-2022-shared-task-gender-rewriting/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This paper reports on a shared task, held at WANLP 2022, on gender rewriting for Modern Standard Arabic. The task takes a sentence and produces versions that fit each combination of speaker gender and listener gender. In Arabic, words that refer to the first or second person carry grammatical gender, so a system that gives one fixed output can encode bias or simply address the user incorrectly.

The organizers based the task on the Arabic Parallel Gender Corpus (APGC v2.1), built from OpenSubtitles 2018. They also commissioned professional linguists to annotate a new 5,000-sentence blind test set.

Following Alhafni et al. (2022b), the task is framed as user-aware grammatical error correction. Systems are scored with the M² scorer (P, R, F0.5) and also with BLEU. Five teams took part, mostly using sentence-level Seq2Seq models. Their outputs often failed to keep the input's word count, and punctuation handling caused much of this noise. With punctuation removed, the best team (CasaNLP) came close to the organizers' earlier baseline.

The paper ends with advice on designing generation-oriented shared tasks so that more teams can join.

## Key points
- **Data.** APGC v2.1 has 80,326 sentences (596,799 words), split into TRAIN 57,603, DEV 6,647 and TEST 16,076 sentences.
  - About 54% of sentences contain gendered words, but only 9.7% of words are gender-specific.
- **Labels.** The notation uses 1M, 1F, 2M and 2F, with B for invariant or ambiguous gender.
  - Sentences get one of nine labels.
  - Words get a basic label or an extended label, with 25 extended labels that mark the base form and the enclitic separately.
- **Blind test set.** The 5,000 new sentences have no overlap with APGC.
  - 21.2% contain both first- and second-person pronouns, 42.3% only first-person, and 36.5% only second-person.
  - Annotators could only reinflect or substitute words, which keeps a 1-to-1 word alignment with the input.
- **Participation.** 15 teams registered and five submitted, from four countries.
- **Results without punctuation.**
  - CasaNLP: F0.5 87.04.
  - Cairo Team: 83.76.
  - UDEL-NLP: 70.22.
  - Qaddoumi: 63.35.
  - Distinguishers: 20.41.
  - Alhafni et al. (2022b) baseline: 87.65.
  - The size of the word-count deviation correlated with the metrics at about −51% for F0.5 and −78% for recall.

## Interesting ideas
- Scoring near-copy rewriting as error correction with edit-level P/R/F0.5 exposes weak systems that BLEU hides; Distinguishers still got BLEU 84 at only F0.5 20.
- Restricting annotators to word reinflection or substitution keeps the outputs aligned 1-to-1 with the input, which let the organizers derive word-level gender labels automatically.
- Checking a structural invariant (equal word count) and correlating its violations with the metric showed that much of the error was format noise, such as punctuation, rather than wrong gender choices.
- The labels are compositional: each person-gender is tagged separately for the base form and the clitic, and an explicit B marks cases where gender is unknown, so the system never has to guess.
- The organizers suggest splitting a hard generation task into an identification subtask and a rewriting subtask, so that partial solutions still count.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (back): Score the text → graph → text compressor on the edits it makes relative to the source text, so faithful copying does not inflate the result.
- **I2** (tokens): Build person/gender/number reinflection test pairs under the same restriction, so morphology and lemma errors can be traced to individual tokens.
- **I3** (learn): Add invariant checks (token count, alignment) as diagnostics during evaluation, to separate pipeline noise from real model failures.
- **I4** (frames): During coreference, record the speaker's and listener's gender as person-indexed features with an explicit "unknown" value, instead of forcing a masculine default.
- **I5** (learn): Evaluate in separate stages (first detect what must change, then rewrite it) to see which step a failure comes from.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [New York University Abu Dhabi](https://md.research.elamur.ai/free/en/universities/new-york-university-abu-dhabi/) — University, Abu Dhabi, United Arab Emirates · 122 works · 4,602 citations
- [University of Delaware](https://md.research.elamur.ai/free/en/universities/university-of-delaware/) — University, Newark, United States · 20 works · 1,056 citations
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## Cite

Cite as: Bashar Alhafni, Nizar Habash, Houda Bouamor, Ossama Obeid, Sultan Alrowili, Daliyah Alzeer, Khawlah M. Alshanqiti, Ahmed ElBakry, Muhammad ElNokrashy, Mohamed Gabr, Abderrahmane Issam, Abdelrahim Qaddoumi, K. Vijay-Shanker and Mahmoud Zyate. 2022. The Shared Task on Gender Rewriting. Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{alhafni2022shared,
  title = {{The Shared Task on Gender Rewriting}},
  author = {Bashar Alhafni, Nizar Habash, Houda Bouamor, Ossama Obeid, Sultan Alrowili, Daliyah Alzeer, Khawlah M. Alshanqiti, Ahmed ElBakry, Muhammad ElNokrashy, Mohamed Gabr, Abderrahmane Issam, Abdelrahim Qaddoumi, K. Vijay-Shanker and Mahmoud Zyate},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.10},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.10}
}
```

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## References

36 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Muhammad Abdul-Mageed, Chiyu Zhang, Houda Bouamor, and Nizar Habash. 2020. [NADI 2020: The first nuanced Arabic dialect identification shared task](https://md.research.elamur.ai/free/en/papers/mageed-2020-nadi-2020-first-nuanced-arabic/). In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 97–110, Barcelona, Spain (Online). Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2010.11334)
2. Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Houda Bouamor, and Nizar Habash. 2021. [NADI 2021: The second nuanced Arabic dialect identification shared task](https://md.research.elamur.ai/free/en/papers/mageed-2021-nadi-2021-second-nuanced-arabic/). In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 244–259, Kyiv, Ukraine (Virtual). Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2103.08466)
3. Ibrahim Abu Farha, Wajdi Zaghouani, and Walid Magdy. 2021. [Overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic](https://md.research.elamur.ai/free/en/papers/farha-2021-overview-wanlp-2021-shared-task/). In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 296–305, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
4. Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2020. Gender-aware reinflection using linguistically enhanced neural models. In Proceedings of the Second Workshop on Gender Bias in Natural Language Processing, pages 139–150, Barcelona, Spain (Online). Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Bashar+Alhafni%2C+Nizar+Habash%2C+and+Houda+Bouamor%2E+2020%2E+Gender%2Daware+reinflection+using+linguistically+enhanced+neural+models%2E+In+Proceedings+of+the+Second+Workshop+on+Gender+Bias+in+Natural+Language+P)
5. Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2022a. [The Arabic parallel gender corpus 2.0: Extensions and analyses](https://md.research.elamur.ai/free/en/papers/alhafni-2022-arabic-parallel-gender-corpus-2/). In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1870–1884, Marseille, France. European Language Resources Association.
6. Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2022b. [User-centric gender rewriting](https://md.research.elamur.ai/free/en/papers/alhafni-2022-user-centric-gender-rewriting/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 618– 631, Seattle, United States. Association for Computational Linguistics. [↗](https://doi.org/10.1184/r1/19728934)
7. Sultan Alrowili and K. Vijay-Shanker. 2022. [Generative approach for gender-rewriting task with ArabicT5](https://md.research.elamur.ai/free/en/papers/alrowili-2022-generative-approach-gender-rewriting-task/). In Proceedings of the Seventh Arabic Natural Language Processing Workshop, Abu Dhabi, UAE. Association for Computational Linguistics.
8. Wissam Antoun, Fady Baly, and Hazem Hajj. 2020. [AraBERT: Transformer-based model for Arabic language understanding](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/). In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 9–15, Marseille, France. European Language Resource Association. [↗](https://doi.org/10.48550/arxiv.2003.00104)
9. Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020. [Language (technology) is power: A critical survey of “bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5454– 5476, Online. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/2020.acl-main.485)
10. Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. [Man is to computer programmer as woman is to homemaker? debiasing word embeddings](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/). In Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc.
11. Houda Bouamor, Sabit Hassan, and Nizar Habash. 2019. [The MADAR shared task on Arabic fine-grained dialect identification](https://md.research.elamur.ai/free/en/papers/bouamor-2019-madar-shared-task-arabic-fine/). In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 199–207, Florence, Italy. Association for Computational Linguistics.
12. Tim Buckwalter. 2002. Buckwalter Arabic morphological analyzer version 1.0. Linguistic Data Consortium (LDC) catalog number LDC2002L49, ISBN 1-58563-257-0. [↗](https://doi.org/10.35111/7vzm-mb15)
13. Daniel Dahlmeier and Hwee Tou Ng. 2012. Better evaluation for grammatical error correction. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 568–572, Montréal, Canada. [find](https://scholar.google.com/scholar?q=Daniel+Dahlmeier+and+Hwee+Tou+Ng%2E+2012%2E+Better+evaluation+for+grammatical+error+correction%2E+In+Proceedings+of+the+2012+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Lin)
14. Nizar Habash, Houda Bouamor, and Christine Chung. 2019. [Automatic gender identification and reinflection in Arabic](https://md.research.elamur.ai/free/en/papers/habash-2019-automatic-gender-identification-reinflection-arabic/). In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 155–165, Florence, Italy.
15. Nizar Y Habash. 2010. Introduction to Arabic natural language processing, volume 3. Morgan & Claypool Publishers. [find](https://scholar.google.com/scholar?q=Nizar+Y+Habash%2E+2010%2E+Introduction+to+Arabic+natural+language+processing%2C+volume+3%2E+Morgan+%26+Claypool+Publishers%2E)
16. Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel. 2019. [It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution](https://md.research.elamur.ai/free/en/papers/maudslay-2019-it-s-all-name-mitigating/). In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5267–5275, Hong Kong, China. [↗](https://doi.org/10.17863/cam.66715)
17. Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, and Nizar Habash. 2021. [The interplay of variant, size, and task type in Arabic pre-trained language models](https://md.research.elamur.ai/free/en/papers/inoue-2021-interplay-variant-size-task-type/). In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 92– 104, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
18. Pierre Lison and Jörg Tiedemann. 2016. [OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles](https://md.research.elamur.ai/free/en/papers/lison-2016-opensubtitles2016-extracting-large-parallel-corpora/). In Proceedings of the Language Resources and Evaluation Conference (LREC), Portorož, Slovenia.
19. Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2018. [Gender bias in neural natural language processing](https://md.research.elamur.ai/free/en/papers/lu-2018-gender-bias-neural-natural-language/).
20. Thomas Manzini, Lim Yao Chong, Alan W Black, and Yulia Tsvetkov. 2019. Black is to criminal as caucasian is to police: Detecting and removing multi-class bias in word embeddings. In 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), pages 615–621, Minneapolis, Minnesota. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Thomas+Manzini%2C+Lim+Yao+Chong%2C+Alan+W+Black%2C+and+Yulia+Tsvetkov%2E+2019%2E+Black+is+to+criminal+as+caucasian+is+to+police%3A+Detecting+and+removing+multi%2Dclass+bias+in+word+embeddings%2E+In+Proceedings+of+the)
21. El Moatez Billah Nagoudi, AbdelRahim Elmadany, and Muhammad Abdul-Mageed. 2022. [AraT5: Text-to-text transformers for Arabic language generation](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 628–647, Dublin, Ireland. Association for Computational Linguistics.
22. Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 2017. JFLEG: A fluency corpus and benchmark for grammatical error correction. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 229–234, Valencia, Spain. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.1702.04066)
23. Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. 2020. [CAMeL tools: An open source python toolkit for Arabic natural language processing](https://md.research.elamur.ai/free/en/papers/obeid-2020-camel-tools-open-source-python/). In Proceedings of The 12th Language Resources and Evaluation Conference, pages 7022–7032, Marseille, France. European Language Resources Association.
24. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [BLEU: a Method for Automatic Evaluation of Machine Translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the Conference of the Association for Computational Linguistics (ACL), pages 311–318, Philadelphia, Pennsylvania, USA.
25. Matt Post. 2018. [A call for clarity in reporting BLEU scores](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/). In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186– 191, Brussels, Belgium.
26. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. [Exploring the limits of transfer learning with a unified text-to-text transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/). Journal of Machine Learning Research, 21(140):1–67. [↗](https://doi.org/10.48550/arxiv.1910.10683)
27. Alla Rozovskaya, Houda Bouamor, Nizar Habash, Wajdi Zaghouani, Ossama Obeid, and Behrang Mohit. 2015. [The second QALB shared task on automatic text correction for Arabic](https://md.research.elamur.ai/free/en/papers/rozovskaya-2015-second-qalb-shared-task-automatic/). In Proceedings of the Second Workshop on Arabic Natural Language Processing, pages 26–35, Beijing, China. Association for Computational Linguistics. 57th Annual Meeting of the Association for Computational Linguistics, pages 1630–1640, Florence, Italy. Association for Computational Linguistics.
28. Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, and Donald Metzler. 2021. Scale efficiently: Insights from pre-training and fine-tuning transformers. [↗](https://doi.org/10.48550/arxiv.2109.10686)
29. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). CoRR, abs/1706.03762. [↗](https://arxiv.org/abs/1706.03762)
30. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. [Transformers: State-of-the-art natural language processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/2020.emnlp-demos.6)
31. Jieyu Zhao, Subhabrata Mukherjee, Saghar Hosseini, Kai-Wei Chang, and Ahmed Hassan Awadallah. 2020. Gender bias in multilingual embeddings and cross-lingual transfer. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2896–2907, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jieyu+Zhao%2C+Subhabrata+Mukherjee%2C+Saghar+Hosseini%2C+Kai%2DWei+Chang%2C+and+Ahmed+Hassan+Awadallah%2E+2020%2E+Gender+bias+in+multilingual+embeddings+and+cross%2Dlingual+transfer%2E+In+Proceedings+of+the+58th+Annual)
32. Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018. [Learning gender-neutral word embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4847–4853, Brussels, Belgium.
33. Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu. 2020. Incorporating bert into neural machine translation. [↗](https://doi.org/10.48550/arxiv.2002.06823)
34. Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell. 2019. [Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology](https://md.research.elamur.ai/free/en/papers/zmigrod-2019-counterfactual-data-augmentation-mitigating-gender/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1651–1661, Florence, Italy.
35. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. [Controlling politeness in neural machine translation via side constraints](https://md.research.elamur.ai/free/en/papers/sennrich-2016-controlling-politeness-neural-machine-translation/). In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 35–40, San Diego, California. Association for Computational Linguistics.
36. Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019. [Mitigating gender bias in natural language processing: Literature review](https://md.research.elamur.ai/free/en/papers/sun-2019-mitigating-gender-bias-natural-language/). In Proceedings of the [↗](https://doi.org/10.48550/arxiv.1906.08976)

*Updated 1 November 2026*