Personalized dialogue systems are an essential step toward better
human-machine interaction. Existing personalized dialogue agents rely on
properly designed conversational datasets, which are mostly monolingual (e.g.,
English), which greatly limits the usage of conversational agents in other
languages. In this paper, we propose a multi-lingual extension of Persona-Chat,
namely XPersona. Our dataset includes persona conversations in six different
languages other than English for building and evaluating multilingual
personalized agents. We experiment with both multilingual and cross-lingual
trained baselines, and evaluate them against monolingual and
translation-pipeline models using both automatic and human evaluation.
Experimental results show that the multilingual trained models outperform the
translation-pipeline and that they are on par with the monolingual models, with
the advantage of having a single model across multiple languages. On the other
hand, the state-of-the-art cross-lingual trained models achieve inferior
performance to the other models, showing that cross-lingual conversation
modeling is a challenging task. We hope that our dataset and baselines will
accelerate research in multilingual dialogue systems.

本研究提出了一个名为 XPersona 的个性化对话数据集，并通过多语言训练的基线模型和跨语言训练的基线模型进行了实验证明。结果表明，多语言训练的模型优于翻译管道模型，并且其性能与单语言训练的模型相当，而跨语言训练的模型则表现出劣于其他模型的性能。该数据集和基线模型有望加速多语言对话系统的研究。