The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers. Specific problems arise when dealing with honorifics, particularly translating from English into languages with formality markers. For example, the sentence "Are you sure?" can be translated in German as "Sind Sie sich sicher?" (formal register) or "Bist du dir sicher?" (informal). Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. This work addresses the problem of learning to control target language attributes, in this case formality, from a small amount of labeled contrastive data. We introduce an annotated dataset (CoCoA-MT) and an associated evaluation metric for training and evaluating formality-controlled MT models for six diverse target languages. We show that we can train formality-controlled models by fine-tuning on labeled contrastive data, achieving high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality.

本篇论文研究如何利用少量有标签的对比数据，学习控制目标语言特征，尤其是正式语言水平的机器翻译问题。通过引入一个注释数据集和相关评估指标，本文证明可以通过微调控制形式的模型，同时保持总体质量高达82%和73%的精确度。

CoCoA-MT：对比控制机器翻译数据集与基准，应用于形式化翻译