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arXiv · 1503.02364

Neural Responding Machine for Short-Text Conversation

Lifeng Shang·Zhengdong Lu·Hang Li
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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.

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Lifeng Shang, Zhengdong Lu, Hang Li. 2015-03-09. Neural Responding Machine for Short-Text Conversation. https://arxiv.org/abs/1503.02364

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