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Neural Responding Machine for Short-Text Conversation

arXiv 1503.02364 ↗First posted 9 Mar 2015cs.CL cs.AI cs.NEcited by 124

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Abstract (arXiv)

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.

Key facts. Cited by 124 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2015.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 42; 2022: 43; 2023: 17; 2024: 14; 2025: 3; 2026: 4.

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38 more
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