去噪自编码器是自动编码器的随机版本,可降低学习功能的风险。去噪自编码器(denoising autoencoder,DAE)是一类接受损坏数据作为输入,并训练来预测原始未被损坏数据作为输入的自编码器。

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In this paper, we present BARTpho with two versions BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and the pre-training scheme of the sequence-to-sequence denoising autoencoder BART, thus especially suitable for generative NLP tasks. We conduct experiments to compare our BARTpho with its competitor mBART on a downstream task of Vietnamese text summarization and show that: in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future research and applications of generative Vietnamese NLP tasks. Our BARTpho models are publicly available at: https://github.com/VinAIResearch/BARTpho

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In this paper, we present BARTpho with two versions BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and the pre-training scheme of the sequence-to-sequence denoising autoencoder BART, thus especially suitable for generative NLP tasks. We conduct experiments to compare our BARTpho with its competitor mBART on a downstream task of Vietnamese text summarization and show that: in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future research and applications of generative Vietnamese NLP tasks. Our BARTpho models are publicly available at: https://github.com/VinAIResearch/BARTpho

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