Machine translation systems with inadequate document understanding can make
errors when translating dropped or neutral pronouns into languages with
gendered pronouns (e.g., English). Predicting the underlying gender of these
pronouns is difficult since it is not marked textually and must instead be
inferred from coreferent mentions in the context. We propose a novel
cross-lingual pivoting technique for automatically producing high-quality
gender labels, and show that this data can be used to fine-tune a BERT
classifier with 92% F1 for Spanish dropped feminine pronouns, compared with
30-51% for neural machine translation models and 54-71% for a non-fine-tuned
BERT model. We augment a neural machine translation model with labels from our
classifier to improve pronoun translation, while still having parallelizable
translation models that translate a sentence at a time.

本研究提出了一种基于跨语言枢轴技术的新颖方法，用于自动生成高质量的性别标签，并展示了这些数据可用于微调 BERT 分类器，该分类器对于西班牙语中丢失的女性代词具有 92％的 F1，相比之下，神经机器翻译模型和非微调 BERT 模型分别为 30-51% 和 54-71％。我们使用来自我们分类器的标签来增强神经机器翻译模型以改进代词翻译，同时仍具有可并行化的翻译模型，可以逐句翻译。