  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.
