Semantic Scholar Open Access 2015 1162 sitasi

Neural Responding Machine for Short-Text Conversation

Lifeng Shang Zhengdong Lu Hang Li

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (3)

Lifeng Shang

Zhengdong Lu

Hang Li

Format Sitasi

APA MLA BibTeX

Shang, L., Lu, Z., Li, H. (2015). Neural Responding Machine for Short-Text Conversation. https://doi.org/10.3115/v1/P15-1152

Akses Cepat

Lihat di Sumber doi.org/10.3115/v1/P15-1152

Informasi Jurnal

Tahun Terbit: 2015
Bahasa: en
Total Sitasi: 1162×
Sumber Database: Semantic Scholar
DOI: 10.3115/v1/P15-1152
Akses: Open Access ✓