# Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It

arXiv: https://arxiv.org/abs/2010.05332 · HTML: https://mlsanity.com/paper/2010.05332/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 30 of 40)

- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (2021)
- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation](https://mlsanity.com/paper/2109.03858/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://mlsanity.com/paper/2211.01355/) (2022)
- [First the worst: Finding better gender translations during beam search](https://mlsanity.com/paper/2104.07429/) (2021)
- [Choose Your Lenses: Flaws in Gender Bias Evaluation](https://mlsanity.com/paper/2210.11471/) (2022)
- [Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation](https://mlsanity.com/paper/2106.00169/) (2021)
- [Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation](https://mlsanity.com/paper/2203.09866/) (2022)
- [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://mlsanity.com/paper/2104.07838/) (2021)
- [Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus](https://mlsanity.com/paper/2310.05294/) (2023)
- [What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns](https://mlsanity.com/paper/2305.16051/) (2023)
- [A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation](https://mlsanity.com/paper/2310.12127/) (2023)
- [GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena](https://mlsanity.com/paper/2108.02854/) (2021)
- [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)
- [Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model](https://mlsanity.com/paper/2305.11140/) (2023)
- [The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses](https://mlsanity.com/paper/2110.09216/) (2021)
- [A Prompt Response to the Demand for Automatic Gender-Neutral Translation](https://mlsanity.com/paper/2402.06041/) (2024)
- [How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation](https://mlsanity.com/paper/2105.13782/) (2021)
- [How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts](https://mlsanity.com/paper/2205.10762/) (2022)
- [Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation](https://mlsanity.com/paper/2305.14016/) (2023)
- [MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2403.19738/) (2024)
- [Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation](https://mlsanity.com/paper/2410.10995/) (2024)
- [Mitigating Gender Bias in Machine Translation through Adversarial Learning](https://mlsanity.com/paper/2203.10675/) (2022)
- [Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation](https://mlsanity.com/paper/2306.04573/) (2023)
- [Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models](https://mlsanity.com/paper/2405.08477/) (2024)
- [Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES](https://mlsanity.com/paper/2310.19345/) (2023)
- [Improving Gender Translation Accuracy with Filtered Self-Training](https://mlsanity.com/paper/2104.07695/) (2021)
- [Gender Inflected or Bias Inflicted: On Using Grammatical Gender Cues for Bias Evaluation in Machine Translation](https://mlsanity.com/paper/2311.03767/) (2023)

## Abstract (arXiv)

Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many existing approaches to this problem seek to control gender inflection in the target language by explicitly or implicitly adding a gender feature to the source sentence, usually at the sentence level. In this paper we propose schemes for incorporating explicit word-level gender inflection tags into NMT. We explore the potential of this gender-inflection controlled translation when the gender feature can be determined from a human reference, or when a test sentence can be automatically gender-tagged, assessing on English-to-Spanish and English-to-German translation. We find that simple existing approaches can over-generalize a gender-feature to multiple entities in a sentence, and suggest effective alternatives in the form of tagged coreference adaptation data. We also propose an extension to assess translations of gender-neutral entities from English given a corresponding linguistic convention, such as a non-binary inflection, in the target language.