DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation - Zhang, Yizhe; Sun, Siqi; Galley, Michel; Chen, Yen-Chun; Brockett, Chris; Gao, Xiang; Gao, Jianfeng; Liu, JJ (Jingjing); Dolan, Bill (arXiv:1911.00536, 2019)
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems. Github link: https://github.com/microsoft/dialogpt