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arXiv · 1911.00536

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

Yizhe Zhang·Siqi Sun·Michel Galley·Yen-Chun Chen·Chris Brockett·Xiang Gao·Jianfeng Gao·Jingjing Liu·Bill Dolan
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Abstract

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.

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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. 2019-11-01. DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation. https://arxiv.org/abs/1911.00536

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