Dialog System Evaluation
ENIGMA
Introduced by Jiang et al. in Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation ApproachENIGMA is an evaluation framework for dialog systems based on Pearson and Spearman's rank correlations between the estimated rewards and the true rewards. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation, making automatic evaluations feasible. More importantly, ENIGMA is model-free and agnostic to the behavior policies for collecting the experience data (see details in Section 2), which significantly alleviates the technical difficulties of modeling complex dialogue environments and human behaviors.
Source: Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Automated Theorem Proving | 2 | 33.33% |
| Classification | 1 | 16.67% |
| Model-based Reinforcement Learning | 1 | 16.67% |
| Reinforcement Learning (RL) | 1 | 16.67% |
| Text Generation | 1 | 16.67% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
