Conversational Response Selection On Dstc7
Metrics
1-of-100 Accuracy
Results
Performance results of various models on this benchmark
Model Name | 1-of-100 Accuracy | Paper Title | Repository |
|---|---|---|---|
| Bi-encoder | 66.3% | Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring | |
| Sequential Attention-based Network | 64.5% | Sequential Attention-based Network for Noetic End-to-End Response Selection | |
| Multi-context ConveRT | 71.2% | ConveRT: Efficient and Accurate Conversational Representations from Transformers | |
| Sequential Inference Models | 60.8% | Building Sequential Inference Models for End-to-End Response Selection | |
| Bi-encoder (v2) | 70.9% | Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring |
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