Customizing machine translation models to comply with fine-grained attributes
such as formality has seen tremendous progress recently. However, current
approaches mostly rely on at least some supervised data with attribute
annotation. Data scarcity therefore remains a bottleneck to democratizing such
customization possibilities to a wider range of languages, lower-resource ones
in particular. Given recent progress in pretrained massively multilingual
translation models, we use them as a foundation to transfer the attribute
controlling capabilities to languages without supervised data. In this work, we
present a comprehensive analysis of transferring attribute controllers based on
a pretrained NLLB-200 model. We investigate both training- and inference-time
control techniques under various data scenarios, and uncover their relative
strengths and weaknesses in zero-shot performance and domain robustness. We
show that both paradigms are complementary, as shown by consistent improvements
on 5 zero-shot directions. Moreover, a human evaluation on a real low-resource
language, Bengali, confirms our findings on zero-shot transfer to new target
languages. The code is
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以预训练的大规模多语种翻译模型为基础，通过研究训练和推理过程中的控制技术，探索了零 - shot 方向上的属性控制能力，并在低资源语言孟加拉进行了人工评估。