We introduce MTG, a new benchmark suite for training and evaluating
multilingual text generation. It is the first-proposed multilingual multiway
text generation dataset with the largest human-annotated data (400k). It
includes four generation tasks (story generation, question generation, title
generation and text summarization) across five languages (English, German,
French, Spanish and Chinese). The multiway setup enables testing knowledge
transfer capabilities for a model across languages and tasks. Using MTG, we
train and analyze several popular multilingual generation models from different
aspects. Our benchmark suite fosters model performance enhancement with more
human-annotated parallel data. It provides comprehensive evaluations with
diverse generation scenarios. Code and data are available at
https://github.com/zide05/MTG.

介绍了一个新的基准测试套件，MTG，用于训练和评估多语言文本生成。该套件包括四个生成任务（故事生成、问题生成、标题生成和文本摘要）和五种语言，可测试模型在不同语言和任务之间的知识转移能力，有利于提高模型性能，并提供多样化的生成方案的全面评估。