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+ Paper: [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://arxiv.org/abs/2212.10233)
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+ ```
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+ @article{https://doi.org/10.48550/arxiv.2212.10233,
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+ doi = {10.48550/ARXIV.2212.10233},
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+ url = {https://arxiv.org/abs/2212.10233},
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+ author = {Wu, Di and Ahmad, Wasi Uddin and Chang, Kai-Wei},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ Pre-training Corpus: [RealNews](https://github.com/rowanz/grover/tree/master/realnews)
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+ Pre-training Details:
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+ - Resume from facebook/bart-base
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+ - Batch size: 2048
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+ - Total steps: 250k
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+ - Learning rate: 3e-4
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+ - LR schedule: polynomial with 10k warmup steps
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+ - Masking ratio: 30%, Poisson lambda = 3.5