Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
This addresses translation ambiguity resolution for users of crosslingual generation systems, though it appears incremental as it builds on existing prompting methods.
The paper tackles the problem of ambiguous translations in crosslingual conditional generation by proposing interactive-chain prompting, a method that uses iterative interactions between translator and user models to resolve ambiguities before final translation. The approach consistently outperforms prompt-based methods with direct background information access, using eight interactions as exemplars.
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.