Fine-grained Gender Control in Machine Translation with Large Language Models
In machine translation, the problem of ambiguously gendered input has been pointed out, where the gender of an entity is not available in the source sentence. To address this ambiguity issue, the task of controlled translation that takes the gender of the ambiguous entity as additional input have been proposed. However, most existing works have only considered a simplified setup of one target gender for input. In this paper, we tackle controlled translation in a more realistic setting of inputs with multiple entities and propose Gender-of-Entity (GoE) prompting method for LLMs. Our proposed method instructs the model with fine-grained entity-level gender information to translate with correct gender inflections. By utilizing four evaluation benchmarks, we investigate the controlled translation capability of LLMs in multiple dimensions and find that LLMs reach state-of-the-art performance in controlled translation. Furthermore, we discover an emergence of gender interference phenomenon when controlling the gender of multiple entities. Finally, we address the limitations of existing gender accuracy evaluation metrics and propose leveraging LLMs as an evaluator for gender inflection in machine translation.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationSentenceTranslationSimilar Papers 제목 키워드 기반
Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection
When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must "guess" this missing information, often leading to incorrect t…
Machine TranslationTranslationFilling Gender \& Number Gaps in Neural Machine Translation with Black-box Context Injection
When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must {``}guess{''} this missing information, often leading to incor…
Machine TranslationTranslationDoes Context Help Mitigate Gender Bias in Neural Machine Translation?
Neural Machine Translation models tend to perpetuate gender bias present in their training data distribution. Context-aware models have been previously suggested as a means to mitigate this type of bias. In this work, we…
Machine TranslationTranslationMT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accuracy in translation can have implications i…
counterfactualEthicsMachine TranslationSentence+1GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation
Targeted evaluations have found that machine translation systems often output incorrect gender in translations, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the po…
Machine TranslationTranslation