模型:
microsoft/DialogRPT-depth
请尝试这个 ➤➤➤ Colab Notebook Demo (click me!)
Context | Response | depth score |
---|---|---|
I love NLP! | Can anyone recommend a nice review paper? | 0.724 |
I love NLP! | Me too! | 0.032 |
深度分数预测回应有多大可能导致长时间的跟进讨论线程。
对话回应有多大可能被点赞 ? 和/或得到回复 ??
这是 DialogRPT 学习预测的内容。它是由 Microsoft Research NLP Group 提出的一组对话回应排序模型,通过对 100+ 万条人类反馈数据进行训练。它可以用来改进现有的对话生成模型(例如 DialoGPT ),通过重新排序生成的回应候选项。
快速链接:
我们考虑了以下任务,并提供了相应的预训练模型。
Task | Description | Pretrained model |
---|---|---|
Human feedback | given a context and its two human responses, predict... | |
updown | ... which gets more upvotes? | 1238321 |
width | ... which gets more direct replies? | 1239321 |
depth | ... which gets longer follow-up thread? | this model |
Human-like (human vs fake) | given a context and one human response, distinguish it with... | |
human_vs_rand | ... a random human response | 12310321 |
human_vs_machine | ... a machine generated response | 12311321 |
请在 our repo 上创建一个问题。
@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} }