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- W2915400310 abstract "While reinforcement learning can effectively improve language generation models, it often suffers from generating incoherent and repetitive phrases [1]. In this paper, we propose a novel repetition normalized adversarial reward to mitigate these problems. Our repetition penalized reward can greatly reduce the repetition rate and adversarial training mitigates generating incoherent phrases. Our model significantly outperforms the baseline model on ROUGE-1 (+3.24), ROUGE-L (+2.25), and a decreased repetition-rate (−4.98%)." @default.
- W2915400310 created "2019-03-02" @default.
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- W2915400310 date "2019-05-01" @default.
- W2915400310 modified "2023-10-14" @default.
- W2915400310 title "A Novel Repetition Normalized Adversarial Reward for Headline Generation" @default.
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- W2915400310 doi "https://doi.org/10.1109/icassp.2019.8683236" @default.
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