Mitigating Gender Bias 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 ...
| Authors | Ioana Grigore, Sergiu Nisioi |
|---|---|
| Primary category | cs.CL |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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