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Demo
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Context | Response | human_vs_rand score |
---|---|---|
I love NLP! | He is a great basketball player. | 0.027 |
I love NLP! | Can you tell me how it works? | 0.754 |
I love NLP! | Me too! | 0.631 |
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:
We considered the following tasks and provided corresponding pretrained models.
Task | Description | Pretrained model |
---|---|---|
Human feedback | given a context and its two human responses, predict... | |
updown |
... which gets more upvotes? | model card |
width |
... which gets more direct replies? | model card |
depth |
... which gets longer follow-up thread? | model card |
Human-like (human vs fake) | given a context and one human response, distinguish it with... | |
human_vs_rand |
... a random human response | this model |
human_vs_machine |
... a machine generated response | model card |
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}
}
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