Pronouns are important determinants of a text's meaning but difficult to translate. This is because pronoun choice can depend on entities described in previous sentences, and in some languages pronouns may be dropped when the referent is inferrable from the context. These issues can lead Neural Machine Translation (NMT) systems to make critical errors on pronouns that impair intelligibility and even reinforce gender bias. We investigate the severity of this pronoun issue, showing that (1) in some domains, pronoun choice can account for more than half of a NMT systems' errors, and (2) pronouns have a disproportionately large impact on perceived translation quality. We then investigate a possible solution: fine-tuning BERT on a pronoun prediction task using chunks of source-side sentences, then using the resulting classifier to repair the translations of an existing NMT model. We offer an initial case study of this approach for the Japanese-English language pair, observing that a small number of translations are significantly improved according to human evaluators.

翻译：Pronouns 是文本含义的重要决定因素, 但很难翻译。 这是因为 pronnoun 选择取决于前几句中描述的实体, 在有些语言中, 当引用从上下文中可以推断出时, ponnouns 可能会被丢弃。 这些问题可以引导神经机器翻译( NMT) 系统在 prononuns 上做出关键错误, 这会损害智能, 甚至强化性别偏见。 我们调查了这个Pronoun 问题的严重性, 表明(1) 在某些领域, 代noun 选择可能占 NMT 系统错误的一半以上, (2) 代nouns 会对感知到的翻译质量产生过大的影响 。 我们然后调查一个可能的解决办法: 利用源边句块对预言预测任务进行微调 BERT, 然后使用由此产生的分类器来修复现有的 NMT 模型的翻译。 我们为日- 英语对提供了这一方法的初步案例研究, 指出, 根据人类评价者的意见, 少数翻译得到显著改进 。