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Dialogrpt Human Vs Machine
DialogRPT is a dialogue response ranking model developed by Microsoft Research, focusing on distinguishing the likelihood of human replies versus machine-generated replies.
Downloads 210
Release Time : 3/2/2022
Model Overview
This model is trained on large-scale human feedback data to predict the likelihood of a dialogue reply coming from a human rather than a machine, and can be applied to improve the output quality of dialogue generation models.
Model Features
Trained on Human Feedback Data
Trained on over 100 million human feedback data points, with strong recognition capabilities for human dialogue patterns.
Multi-task Support
The DialogRPT series supports multiple dialogue ranking tasks, including upvote prediction, reply count prediction, and dialogue depth prediction.
Improve Dialogue Generation
Can be used to re-rank generated reply candidates, enhancing the output quality of existing dialogue generation models (e.g., DialoGPT).
Model Capabilities
Dialogue Response Ranking
Human vs. Machine Reply Differentiation
Dialogue Quality Evaluation
Use Cases
Dialogue System Improvement
Dialogue Generation Model Optimization
Used to filter and optimize output replies from dialogue generation models like DialoGPT
Improves the human-like quality and dialogue quality of generated replies
Dialogue Quality Evaluation
Automatic Dialogue Evaluation
Evaluates the human-like quality of replies in dialogue systems
Provides a score between 0-1, predicting the likelihood of a reply coming from a human rather than a machine
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