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• W2963963856 abstract "We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large amount of one-round conversation data collected from a microblogging service. Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming state-of-the-arts in the same setting, including retrieval-based and SMT-based models." @default.
• W2963963856 created "2019-07-30" @default.
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• W2963963856 date "2015-01-01" @default.
• W2963963856 modified "2023-10-13" @default.
• W2963963856 title "Neural Responding Machine for Short-Text Conversation" @default.
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• W2963963856 doi "https://doi.org/10.3115/v1/p15-1152" @default.
• W2963963856 hasPublicationYear "2015" @default.
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