DialoGPT was proposed in DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. It’s a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.
The abstract from the paper is the following:
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.
DialoGPT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.
DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems.
DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on DialoGPT’s model card.
In order to train or fine-tune DialoGPT, one can use causal language modeling training. To cite the official paper: We follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,…, x_N (N is the sequence length), ended by the end-of-text token. For more information please confer to the original paper.
DialoGPT’s architecture is based on the GPT2 model, so one can refer to GPT2’s documentation page.
The original code can be found here.