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• W3104358322 abstract "Paraphrase generation is an important problem in Natural Language Processing that has been addressed with neural network-based approaches recently. This paper presents an adversarial framework to address the paraphrase generation problem in English. Unlike previous methods, we employ the discriminator output as penalization instead of using policy gradients, and we propose a global discriminator to avoid the Monte-Carlo search. In addition, this work use and compare different settings of input representation. We compare our methods to some baselines in the Quora question pairs dataset. The results show that our framework is competitive against the previous benchmarks." @default.
• W3104358322 created "2020-11-23" @default.
• W3104358322 creator A5011968554 @default.
• W3104358322 creator A5023209130 @default.
• W3104358322 date "2020-01-01" @default.
• W3104358322 modified "2023-09-24" @default.
• W3104358322 title "Paraphrase Generation via Adversarial Penalizations" @default.
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• W3104358322 doi "https://doi.org/10.18653/v1/2020.wnut-1.32" @default.
• W3104358322 hasPublicationYear "2020" @default.
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