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.
翻译:我们展示了一个大型的金枪鱼可神经对口反应生成模型,DialoGPT(对话基因组培训前变压器)。在2005至2017年期间,DialoGPT从Reddit评论链中抽取了147M式谈话式交流,在2005至2017年期间,DialoGPT扩展了Huging脸 PyTorrch变压器,以便在单向对话环境中自动和人文评估方面达到接近人类的性能。我们展示了利用DialoGPT的对口系统比强大的基线系统产生更贴切、内容丰富和符合背景的反应。预先培训的模型和培训管道被公开发布,以促进神经反应生成研究以及开发更智能的开放式对话系统。