| DialoGPT | |
| Overview | |
| 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. | |
| The original code can be found here. | |
| Usage tips | |
| 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. | |
| Training: | |
| 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, refer to GPT2's documentation page for API reference and examples. | |