Bootstrapping Multilingual Semantic Parsers using Large Language Models
This addresses the challenge of training task-specific multilingual models for low-resource languages by reducing reliance on expensive human-annotated translations, though it is incremental as it builds on existing translate-train paradigms with LLMs.
The paper tackles the problem of costly and brittle translation services for multilingual semantic parsing by using large language models (LLMs) to translate English datasets into multiple languages via few-shot prompting, showing that this method outperforms a strong translate-train baseline on 41 out of 50 languages across two datasets.
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models. However, for many low-resource languages, the availability of a reliable translation service entails significant amounts of costly human-annotated translation pairs. Further, translation services may continue to be brittle due to domain mismatch between task-specific input text and general-purpose text used for training translation models. For multilingual semantic parsing, we demonstrate the effectiveness and flexibility offered by large language models (LLMs) for translating English datasets into several languages via few-shot prompting. Through extensive comparisons on two public datasets, MTOP and MASSIVE, spanning 50 languages and several domains, we show that our method of translating data using LLMs outperforms a strong translate-train baseline on 41 out of 50 languages. We study the key design choices that enable more effective multilingual data translation via prompted LLMs.