Translating from languages without productive grammatical gender like English
into gender-marked languages is a well-known difficulty for machines. This
difficulty is also due to the fact that the training data on which models are
built typically reflect the asymmetries of natural languages, gender bias
included. Exclusively fed with textual data, machine translation is
intrinsically constrained by the fact that the input sentence does not always
contain clues about the gender identity of the referred human entities. But
what happens with speech translation, where the input is an audio signal? Can
audio provide additional information to reduce gender bias? We present the
first thorough investigation of gender bias in speech translation, contributing
with: i) the release of a benchmark useful for future studies, and ii) the
comparison of different technologies (cascade and end-to-end) on two language
directions (English-Italian/French).

研究了语音翻译中存在的性别偏见问题，并基于英意 / 英法语言方向比较了级联与端到端技术，着重于探究如何利用音频信息来解决自然语言中的性别偏见问题。

性别危险？在 MuST-SHE 语料库上评估语音翻译技术

Gender in Danger? Evaluating Speech Translation Technology on the  MuST-SHE Corpus

In recent years, pretrained word embeddings have proved useful for multimodal
neural machine translation (NMT) models to address the shortage of available
datasets. However, the integration of pretrained word embeddings has not yet
been explored extensively. Further, pretrained word embeddings in high
dimensional spaces have been reported to suffer from the hubness problem.
Although some debiasing techniques have been proposed to address this problem
for other natural language processing tasks, they have seldom been studied for
multimodal NMT models. In this study, we examine various kinds of word
embeddings and introduce two debiasing techniques for three multimodal NMT
models and two language pairs -- English-German translation and English-French
translation. With our optimal settings, the overall performance of multimodal
models was improved by up to +1.93 BLEU and +2.02 METEOR for English-German
translation and +1.73 BLEU and +0.95 METEOR for English-French translation.

本研究探讨了预训练词嵌入及其在多模式神经机器翻译模型中的应用，引入两种去偏差技术，并在英德翻译和英法翻译两种语言对上获得了最多 + 1.93 BLEU 和 + 2.02 METEOR 的性能提升。