# First the worst: Finding better gender translations during beam search

arXiv: https://arxiv.org/abs/2104.07429 · HTML: https://mlsanity.com/paper/2104.07429/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 54 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [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)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Evaluating Gender Bias in Machine Translation](https://mlsanity.com/paper/1906.00591/) (2019)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019)
- [On NMT Search Errors and Model Errors: Cat Got Your Tongue?](https://mlsanity.com/paper/1908.10090/) (2019)
- [Getting Gender Right in Neural Machine Translation](https://mlsanity.com/paper/1909.05088/) (2019)
- [Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://mlsanity.com/paper/2004.04498/) (2020)
- [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018)
- [Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques](https://mlsanity.com/paper/1901.03116/) (2019)
- [Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation](https://mlsanity.com/paper/1906.12068/) (2019)
- [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)
- [Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection](https://mlsanity.com/paper/1903.03467/) (2019)
- [Decoding and Diversity in Machine Translation](https://mlsanity.com/paper/2011.13477/) (2020)
- [Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions](https://mlsanity.com/paper/1610.01108/) (2016)
- [DEMorphy, German Language Morphological Analyzer](https://mlsanity.com/paper/1803.00902/) (2018)
- [Syntactically Guided Neural Machine Translation](https://mlsanity.com/paper/1605.04569/) (2016)

## Built on by (most cited first, 29 of 29)

- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (2021)
- [Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes](https://mlsanity.com/paper/2402.01981/) (2024)
- [Fairness in Large Language Models: A Taxonomic Survey](https://mlsanity.com/paper/2404.01349/) (2024)
- [FairSteer: Inference Time Debiasing for LLMs with Dynamic Activation Steering](https://mlsanity.com/paper/2504.14492/) (2025)
- [Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization](https://mlsanity.com/paper/2210.04492/) (2022)
- [On Learning Fairness and Accuracy on Multiple Subgroups](https://mlsanity.com/paper/2210.10837/) (2022)
- [SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents](https://mlsanity.com/paper/2411.03284/) (2024)
- [Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey](https://mlsanity.com/paper/2406.05392/) (2024)
- [Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges](https://mlsanity.com/paper/2301.10075/) (2023)
- [Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus](https://mlsanity.com/paper/2310.05294/) (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)
- [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)
- [Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies](https://mlsanity.com/paper/2312.01509/) (2023)
- [Potential and Challenges of Model Editing for Social Debiasing](https://mlsanity.com/paper/2402.13462/) (2024)
- [Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation](https://mlsanity.com/paper/2306.04573/) (2023)
- [Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES](https://mlsanity.com/paper/2310.19345/) (2023)
- [BiasFilter: An Inference-Time Debiasing Framework for Large Language Models](https://mlsanity.com/paper/2505.23829/) (2025)
- [Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation](https://mlsanity.com/paper/2511.21517/) (2025)
- [A Group Fairness Lens for Large Language Models](https://mlsanity.com/paper/2312.15478/) (2023)
- [Generating Gender Alternatives in Machine Translation](https://mlsanity.com/paper/2407.20438/) (2024)
- [Reducing Gender Bias in Machine Translation through Counterfactual Data Generation](https://mlsanity.com/paper/2311.16362/) (2023)
- [Integrating gender inclusivity into large language models via instruction tuning](https://mlsanity.com/paper/2508.18466/) (2025)
- [Cultural Fidelity in English-to-Hindi Translation: A Preservation-Fluency Frontier for Gender Recoverability](https://mlsanity.com/paper/2605.27654/) (2026)
- [When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation](https://mlsanity.com/paper/2607.07937/) (2026)
- [Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution](https://mlsanity.com/paper/2607.22546/) (2026)
- [Quit While You're Ahead: Quit for Efficient Candidate Generation in Machine Translation Reranking](https://mlsanity.com/paper/2609.00588/) (2026)
- [Collective Bias Mitigation via Model Routing and Collaboration](https://mlsanity.com/paper/2610.03240/) (2026)

## Abstract (arXiv)

Neural machine translation inference procedures like beam search generate the most likely output under the model. This can exacerbate any demographic biases exhibited by the model. We focus on gender bias resulting from systematic errors in grammatical gender translation, which can lead to human referents being misrepresented or misgendered. Most approaches to this problem adjust the training data or the model. By contrast, we experiment with simply adjusting the inference procedure. We experiment with reranking nbest lists using gender features obtained automatically from the source sentence, and applying gender constraints while decoding to improve nbest list gender diversity. We find that a combination of these techniques allows large gains in WinoMT accuracy without requiring additional bilingual data or an additional NMT model.