When translating words referring to the speaker, speech translation (ST)
systems should not resort to default masculine generics nor rely on potentially
misleading vocal traits. Rather, they should assign gender according to the
speakers' preference. The existing solutions to do so, though effective, are
hardly feasible in practice as they involve dedicated model re-training on
gender-labeled ST data. To overcome these limitations, we propose the first
inference-time solution to control speaker-related gender inflections in ST.
Our approach partially replaces the (biased) internal language model (LM)
implicitly learned by the ST decoder with gender-specific external LMs.
Experiments on en->es/fr/it show that our solution outperforms the base models
and the best training-time mitigation strategy by up to 31.0 and 1.6 points in
gender accuracy, respectively, for feminine forms. The gains are even larger
(up to 32.0 and 3.4) in the challenging condition where speakers' vocal traits
conflict with their gender.

用于语音翻译系统的控制说话者性别变化的解决方案通过使用性别特定的外部语言模型，在性别准确性方面比基本模型和最好的训练时间缓解策略分别提高了 31.0 和 1.6 个百分点，特别是在说话者的声音特征与性别相冲突的情况下，增益甚至更大（最多 32.0 和 3.4 个百分点）。

将语言模型整合到直接语音翻译中：一种控制性别屈折的推理时间解决方案

Integrating Language Models into Direct Speech Translation: An  Inference-Time Solution to Control Gender Inflection

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 in terms of
output fluency, translation accuracy, and ethics. In this paper, we introduce
MT-GenEval, a benchmark for evaluating gender accuracy in translation from
English into eight widely-spoken languages. MT-GenEval complements existing
benchmarks by providing realistic, gender-balanced, counterfactual data in
eight language pairs where the gender of individuals is unambiguous in the
input segment, including multi-sentence segments requiring inter-sentential
gender agreement. Our data and code is publicly available under a CC BY SA 3.0
license.

这篇论文介绍了 MT-GenEval，一个用于评估英语到八种广泛使用语言的翻译中的性别准确性的基准测试，通过提供现实的、性别平衡的、反事实数据来补充现有基准测试，包括需要跨句子的性别协议的多句子片段。