Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus on a range of Natural Language Understanding (NLU) tasks, without considering the Natural Language Generation (NLG) models. In this paper, we present the General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. For each task, we continue to design three subtasks in terms of task difficulty (GLGE-Easy, GLGE-Medium, and GLGE-Hard). This introduces 24 subtasks to comprehensively compare model performance. To encourage research on pretraining and transfer learning on NLG models, we make GLGE publicly available and build a leaderboard with strong baselines including MASS, BART, and ProphetNet\footnote{The source code and dataset will be publicly available at https://github.com/microsoft/glge.

中文翻译：

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GLGE：新的通用语言生成评估基准

诸如GLUE和SuperGLUE之类的多任务基准已经推动了自然语言处理（NLP）的预训练和转移学习的巨大进步。这些基准测试主要侧重于一系列自然语言理解（NLU）任务，而没有考虑自然语言生成（NLG）模型。在本文中，我们介绍了通用语言生成评估（GLGE），这是一个新的多任务基准，用于评估八种语言生成任务中NLG模型的泛化能力。对于每个任务，我们将继续按照任务难度设计三个子任务（GLGE-Easy，GLGE-Medium和GLGE-Hard）。这引入了24个子任务来全面比较模型性能。为了鼓励对NLG模型进行预训练和转移学习的研究，