titanicc/titanicdrpt

Demo

Please try this ➤➤➤ Colab Notebook Demo (click me!)

The human_vs_rand score predicts how likely the response is corresponding to the given context, rather than a random response.

DialogRPT-human-vs-rand

Dialog Ranking Pretrained Transformers

How likely a dialog response is upvoted 👍 and/or gets replied 💬?

This is what **DialogRPT** is learned to predict. It is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on 100 + millions of human feedback data. It can be used to improve existing dialog generation model (e.g., DialoGPT) by re-ranking the generated response candidates.

Quick Links:

• —EMNLP'20 Paper
• —Dataset, training, and evaluation
• —Colab Notebook Demo

We considered the following tasks and provided corresponding pretrained models.

Contact:

Please create an issue on our repo

Citation:

@inproceedings{gao2020dialogrpt,
    title={Dialogue Response RankingTraining with Large-Scale Human Feedback Data},
    author={Xiang Gao and Yizhe Zhang and Michel Galley and Chris Brockett and Bill Dolan},
    year={2020},
    booktitle={EMNLP}
}