Neural Responding Machine for Short-Text Conversation

2015/03/09 by Lifeng Shang, Zhengdong Lu, Shang, Lifeng +3 · 21 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Neural and Evolutionary Computing (cs.NE) #Speech Recognition and Synthesis #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1503.02364

openalex publication_date 2015/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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