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。

可扩展的跨语言支枢词建模代词性别用于翻译