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 Microsoft Corporation, Redmond, WA, USA A collaboration between Microsoft Research and Microsoft Dynamics 365 AI Research.
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.
中文速览
微软研究团队为了让机器能在开放领域对话中生成更自然、更有内容的回复，将GPT-2这一大规模预训练语言模型迁移到对话场景，从Reddit十二年的评论链中抽取了1.47亿条对话训练数据，训练出了DialoGPT这个最大达7.62亿参数的对话生成模型。在建模上，他们把多轮对话拼接成一段长文本交给Transformer做自回归语言建模，并引入互信息最大化（Maximum Mutual Information, MMI）重排机制来惩罚那些泛泛而谈、毫无信息量的回复。在DSTC-7等公开基准和自建的6000条多参考测试集上，DialoGPT在BLEU、NIST等自动评估指标以及人工评估中均达到接近人类水平的表现，甚至在部分指标上超越人类基线。这项工作的重要意义在于，它开源了预训练模型和完整训练流程，为后续研究者构建更智能的开放域对话系统提供了一个强大且易于复用的基础。
原文 arXiv:1911.00536;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1911.00536v3