Conversational Response Selection On Dstc7
Metriken
1-of-100 Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| Multi-context ConveRT | 71.2% | ConveRT: Efficient and Accurate Conversational Representations from Transformers |
| Bi-encoder (v2) | 70.9% | Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring |
| 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 |
| Sequential Inference Models | 60.8% | Building Sequential Inference Models for End-to-End Response Selection |
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