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• W3110048753 abstract "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 (The source code and dataset are publicly available at https://github.com/microsoft/glge)." @default.
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• W3110048753 date "2020-11-24" @default.
• W3110048753 modified "2023-09-30" @default.
• W3110048753 title "GLGE: A New General Language Generation Evaluation Benchmark" @default.
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• W3110048753 doi "https://doi.org/10.48550/arxiv.2011.11928" @default.
• W3110048753 hasPublicationYear "2020" @default.
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