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I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian](https://huggingface.co/papers/2606.31718) (2026)\n* [Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study](https://huggingface.co/papers/2608.03480) (2026)\n* [A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books](https://huggingface.co/papers/2607.22376) (2026)\n* [Estimating Grammatical Gender Directions in Contextual Embeddings under Controlled and Natural Contexts](https://huggingface.co/papers/2606.30152) (2026)\n* [Neural Machine Translation for Low-Resource Tangkhul-English](https://huggingface.co/papers/2606.25365) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2606.31718\">Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2608.03480\">Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.22376\">A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2606.30152\">Estimating Grammatical Gender Directions in Contextual Embeddings under Controlled and Natural Contexts</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2606.25365\">Neural Machine Translation for Low-Resource Tangkhul-English</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code>@librarian-bot recommend</code></p>\n","updatedAt":"2026-08-15T01:35:01.006Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":378,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.708220899105072},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.08606","authors":[{"_id":"6a7f2fe1f747ea94019af535","name":"Ioana Grigore","hidden":false},{"_id":"6a7f2fe1f747ea94019af536","name":"Sergiu Nisioi","hidden":false}],"publishedAt":"2026-08-09T00:00:00.000Z","submittedOnDailyAt":"2026-08-14T00:00:00.000Z","title":"Mitigating Gender Bias in English to Romanian Machine Translation","submittedOnDailyBy":{"_id":"66350219843f549fdac86347","avatarUrl":"/avatars/1ad7aa4e8e6d80c5d50bf4de502f11a4.svg","isPro":false,"fullname":"Matteo Negri","user":"MNegri","type":"user","name":"MNegri"},"summary":"Machine translation (MT) systems often fail to correctly translate gender, especially when converting from a gender-neutral language like English to a gendered target language such as Romanian. This bias results in translations that default to masculine forms or reinforce gender stereotypes. We propose a hybrid pipeline to mitigate this issue by combining large language model (LLM)-based gender classification with neural machine translation (NMT). Our system uses a fine-tuned LLM to detect the intended gender of target words in English sentences and insert inline gender hint tags. These tagged sentences are then passed to a Transformer model fine-tuned to generate morphologically correct Romanian translations. To support this, we introduce three novel datasets for gender disambiguation and translation. Our approach improves gender accuracy on the WinoMT and WinoGender benchmarks by over 40 percentage points compared to a baseline MT system. This is the first method to explicitly address and evaluate gender bias in English-Romanian MT using both LLM inference and tag-aware translation.","upvotes":0,"discussionId":"6a7f2fe1f747ea94019af537","ai_summary":"A hybrid pipeline combining LLM gender classification with tag-aware neural translation improves gender accuracy in English-to-Romanian machine translation.","ai_keywords":["large language model","gender classification","neural machine translation","Transformer","gender hint tags","gender disambiguation","WinoMT","WinoGender"],"ai_summary_model":"thinkingmachines/Inkling-Small"},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2608/2608.08606.md","query":{}}">
Mitigating Gender Bias in English to Romanian Machine Translation
Abstract
A hybrid pipeline combining LLM gender classification with tag-aware neural translation improves gender accuracy in English-to-Romanian machine translation.
Machine translation (MT) systems often fail to correctly translate gender, especially when converting from a gender-neutral language like English to a gendered target language such as Romanian. This bias results in translations that default to masculine forms or reinforce gender stereotypes. We propose a hybrid pipeline to mitigate this issue by combining large language model (LLM)-based gender classification with neural machine translation (NMT). Our system uses a fine-tuned LLM to detect the intended gender of target words in English sentences and insert inline gender hint tags. These tagged sentences are then passed to a Transformer model fine-tuned to generate morphologically correct Romanian translations. To support this, we introduce three novel datasets for gender disambiguation and translation. Our approach improves gender accuracy on the WinoMT and WinoGender benchmarks by over 40 percentage points compared to a baseline MT system. This is the first method to explicitly address and evaluate gender bias in English-Romanian MT using both LLM inference and tag-aware translation.
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