As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns,
we need more research on identity-inclusive NLP. Exclusion is particularly
harmful in one of the most popular NLP applications, machine translation (MT).
Wrong pronoun translations can discriminate against marginalized groups, e.g.,
non-binary individuals (Dev et al., 2021). In this ``reality check'', we study
how three commercial MT systems translate 3rd-person pronouns. Concretely, we
compare the translations of gendered vs. gender-neutral pronouns from English
to five other languages (Danish, Farsi, French, German, Italian), and vice
versa, from Danish to English. Our error analysis shows that the presence of a
gender-neutral pronoun often leads to grammatical and semantic translation
errors. Similarly, gender neutrality is often not preserved. By surveying the
opinions of affected native speakers from diverse languages, we provide
recommendations to address the issue in future MT research.

本文针对以往自然语言处理领域所忽略的 neopronouns 等全新形式三人称代词使用方式及其对包容性的影响展开探究，特别关注了机器翻译对于被边缘化团体如非二进制人群等的歧视问题。发现中性代词经常导致语法和语义翻译错误，因此提供了针对这些问题的未来研究建议。

如何处理（新）代词：商业机器翻译的失败

What about em? How Commercial Machine Translation Fails to Handle  (Neo-)Pronouns

Popular Neural Machine Translation model training uses strategies like
backtranslation to improve BLEU scores, requiring large amounts of additional
data and training. We introduce a class of conditional
generative-discriminative hybrid losses that we use to fine-tune a trained
machine translation model. Through a combination of targeted fine-tuning
objectives and intuitive re-use of the training data the model has failed to
adequately learn from, we improve the model performance of both a
sentence-level and a contextual model without using any additional data. We
target the improvement of pronoun translations through our fine-tuning and
evaluate our models on a pronoun benchmark testset. Our sentence-level model
shows a 0.5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets,
while our contextual model achieves the best results, improving from 31.81 to
32 BLEU on WMT14 De-En testset, and from 32.10 to 33.13 on the IWSLT13 De-En
testset, with corresponding improvements in pronoun translation. We further
show the generalizability of our method by reproducing the improvements on two
additional language pairs, Fr-En and Cs-En. Code available at
<this https URL>.

本研究引入了一类新的条件生成 - 判别混合 Loss，用于微调经过训练的机器翻译模型，通过有针对性的微调目标和直观地重复利用训练数据，从而在不使用任何其他数据的情况下提高了模型的性能。我们针对代词翻译问题进行微调，并在代词基准测试集上对模型进行评估。结果表明我们的模型在几个测试集上都实现了较高的 BLEU 分数，特别是在 IWSLT13 De-En testset 上达到了最佳效果。同时我们还在 Fr-En 和 Cs-En 上重现了该方法的改进效果。