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

About

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.

Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan• 2019

Related benchmarks

TaskDatasetResultRank
Dialogue SummarizationSamSum (test)
ROUGE-216.58
80
Abstractive dialogue summarizationSamSum (test)
ROUGE-L38.42
53
Emotional Support ConversationESConv (test)
BLEU-25.52
44
Conversational PerformanceREDIAL (test)
Distinct-362.09
37
ConversationINSPIRED
Distinct-22.408
27
RecommendationREDIAL
R@1017.3
24
Paraphrase GenerationQQP (test)
BLEU-228.45
22
Conversational PerformanceTG-REDIAL (test)
Dist-21.1881
21
Dialogue GenerationDouban (test)
BLEU-10.0953
20
Intent RecognitionOOS (test)
Overall Accuracy83.9
19
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