First the Worst: Finding Better Gender Translations During Beam Search (2022.findings-acl)
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| Challenge: | Neural language generation models optimized by likelihood tend towards 'safe' word choice. |
| Approach: | They propose to use beam search to improve gender diversity in n-best lists and rerank n best lists using gender features obtained from the source sentence to address this problem. |
| Outcome: | The proposed approach improves gender diversity in n-best lists and reranks n best lists using gender features obtained from the source sentence. |
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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 . |
| Outcome: | The proposed method compares different technologies on two languages, English and French. |
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation (D18-1)
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| Challenge: | Beam search is widely used in neural machine translation, but beam sizes larger than 5 hurt translation quality. |
| Approach: | They propose to use beam search to improve translation quality by using hyperparameter-free methods that outperform the widely-used heuristic of length normalization by +2.0 BLEU. |
| Outcome: | The proposed methods outperform the widely-used heuristic on Chinese-to-English translation and achieve the best results among all methods. |
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem (2020.acl-main)
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| Challenge: | Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men. |
| Approach: | They propose a lattice-rescoring scheme which allows a trade-off between general translation quality and bias reduction during adaptation and inference time. |
| Outcome: | The proposed approach outperforms all systems evaluated on WinoMT with no degradation of general test set BLEU. |
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation (2023.emnlp-main)
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| Challenge: | Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, but current research focuses on standard performance benchmarks, leaving compelling fairness and ethical considerations behind. |
| Approach: | They propose a method to mitigate gender bias in machine translation by using a corpus of machine translations from the WinoMT corpus. |
| Outcome: | The proposed model can solve multiple NLP tasks when prompted, but it lacks fairness and ethical considerations. |
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations (2025.findings-acl)
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| Challenge: | addressing gender bias and maintaining logical coherence in machine translation remains challenging, especially when translating between natural gender languages, like English, and genderless languages, such as Persian, Indonesian, and Finnish. |
| Approach: | They propose a dataset to assess translation systems' performance in six low- to mid-resource languages and a translation dataset to examine gender bias and logical coherence. |
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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. |
| Outcome: | The proposed method extends the existing dataset to 108K diverse English sentences. |
Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
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| Challenge: | Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. |
| Approach: | They propose to use probing methods to assess gender encoding across ST models. |
| Outcome: | The proposed models capture speaker-specific features, including gender, while older models do not . low gender encoding capabilities result in systems’ tendency toward a masculine default, a translation bias that is more pronounced in newer architectures. |
Automatically Identifying Gender Issues in Machine Translation using Perturbations (2020.findings-emnlp)
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| Challenge: | a novel approach to machine translation has addressed outstanding challenges, including the modeling and treatment of gendered language. |
| Approach: | They propose a method to mine examples from real world data to explore challenges for deployed systems. |
| Outcome: | The proposed method exposes where model representations are gendered and the unintended consequences of genderes in downstream applications. |
How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts. (2022.findings-emnlp)
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| Challenge: | Neural Machine Translation systems are prone to gender biases in their learned representations. |
| Approach: | They propose to use contextual sentences to correct gender bias in Neural Machine Translation models. |
| Outcome: | The proposed method can be used to build better, bias-free translation systems. |
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. |
| Outcome: | The proposed approach improves gender accuracy without damaging generic quality on translations from English into five languages. |