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.
翻译:尽管经过培训的多语文模式展示了跨语言的交叉概括,但将英文数据集跨越多种语文的翻译培训模式仍然是培训具体任务多语种模式的一个关键机制,然而,对于许多低资源语言而言,可靠的翻译服务的提供需要大量昂贵的人工附加说明的翻译配对。此外,由于特定任务投入文本和培训翻译模式所用一般用途文本之间在域上的不匹配,翻译服务可能继续不畅。关于多语种语义拼法,我们展示了大语言模式(LLLMs)在通过几发提示将英文数据集翻译成几种语文方面的有效性和灵活性。通过广泛比较两种公共数据集(MTOP和MASSive,涵盖50种语言和若干领域),我们表明,我们用LMS翻译数据的方法超越了50种语言中的41种的强有力的翻译-引力基线。我们研究了能够通过激励LLMs进行更有效的多语种数据翻译的关键设计选择。