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

Yizhe Zhang,Siqi Sun,6 Authors,W. Dolan

2019 · DOI: 10.18653/V1/2020.ACL-DEMOS.30
Annual Meeting of the Association for Computational Linguistics · 1,739 Citations

TLDR

It is shown that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.

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.