Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rather limited. This is partly due to the high cost of obtaining non-English conversational data, which results in limited coverage. In this work, we introduce XSGD, a parallel and large-scale multilingual conversation dataset that we created by translating the English-only Schema-Guided Dialogue (SGD) dataset (Rastogi et al., 2020) into 105 other languages. XSGD contains approximately 330k utterances per language. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts. We also investigate two different classifiers: NLI-based and vanilla classifiers, and test cross-lingual capability enabled by the aligned prompts. We evaluate our model's cross-lingual generalization capabilities on two conversation tasks: slot-filling and intent classification. Our results demonstrate the strong and efficient modeling ability of NLI-based classifiers and the large cross-lingual transfer improvements achieved by our aligned prompts, particularly in few-shot settings.

翻译：跨语言迁移学习已在高资源语言（如英语）训练的语言模型中被广泛研究，用于众多自然语言处理任务，但针对对话任务的研究仍相当有限。部分原因是获取非英语对话数据的高昂成本导致语种覆盖范围受限。本研究提出了XSGD——一个通过将纯英语模式引导对话数据集（Schema-Guided Dialogue, SGD）（Rastogi等人，2020年）翻译成105种其他语言而构建的大规模平行多语言对话数据集。该数据集每种语言包含约33万条语句。为实现对齐的跨语言表征，我们开发了一种基于提示微调的高效方法，用于学习对齐提示。同时，我们研究了两种不同的分类器：基于自然语言推理的分类器与标准分类器，并测试了由对齐提示赋予的跨语言能力。我们在槽填充和意图分类两个对话任务上评估了模型的跨语言泛化性能。实验结果表明，基于自然语言推理的分类器具有强大且高效的建模能力，而我们的对齐提示在跨语言迁移——尤其是少样本场景下——带来了显著的性能提升。