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.
@article{arxiv.1503.02364,
title = {Neural Responding Machine for Short-Text Conversation},
author = {Lifeng Shang and Zhengdong Lu and Hang Li},
journal= {arXiv preprint arXiv:1503.02364},
year = {2015}
}