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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Poolingformer: Long Document Modeling with Pooling Attention ... Zhang, Hang; Gong, Yeyun; Shen, Yelong. - : arXiv, 2021 BASE Show details 2 ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation ... Qi, Weizhen; Gong, Yeyun; Yan, Yu. - : arXiv, 2021 BASE Show details 3 XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation ... Liang, Yaobo; Duan, Nan; Gong, Yeyun. - : arXiv, 2020 BASE Show details 4 GLGE: A New General Language Generation Evaluation Benchmark ... Liu, Dayiheng; Yan, Yu; Gong, Yeyun; Qi, Weizhen; Zhang, Hang; Jiao, Jian; Chen, Weizhu; Fu, Jie; Shou, Linjun; Gong, Ming; Wang, Pengcheng; Chen, Jiusheng; Jiang, Daxin; Lv, Jiancheng; Zhang, Ruofei; Wu, Winnie; Zhou, Ming; Duan, Nan. - : arXiv, 2020 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). ... : Findings of Association for Computational Linguistics. ACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2011.11928 https://arxiv.org/abs/2011.11928 BASE Hide details

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