Crosslingual conditional generation (e.g., machine translation) has long
enjoyed the benefits of scaling. Nonetheless, there are still issues that scale
alone may not overcome. A source query in one language, for instance, may yield
several translation options in another language without any extra context. Only
one translation could be acceptable however, depending on the translator's
preferences and goals. Choosing the incorrect option might significantly affect
translation usefulness and quality. We propose a novel method interactive-chain
prompting -- a series of question, answering and generation intermediate steps
between a Translator model and a User model -- that reduces translations into a
list of subproblems addressing ambiguities and then resolving such subproblems
before producing the final text to be translated. To check ambiguity resolution
capabilities and evaluate translation quality, we create a dataset exhibiting
different linguistic phenomena which leads to ambiguities at inference for four
languages. To encourage further exploration in this direction, we release all
datasets. We note that interactive-chain prompting, using eight interactions as
exemplars, consistently surpasses prompt-based methods with direct access to
background information to resolve ambiguities.

提出了一种交互式链式提示方法，通过一系列问题、回答和生成步骤来解决跨语言条件生成中的翻译歧义问题，并通过制作数据集来评估其解决模糊性的能力和评估翻译质量。