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Slot Filling On Snips
Metrics
F1
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| CTRAN | 98.3 | CTRAN: CNN-Transformer-based Network for Natural Language Understanding | |
| Stack-Propagation (+BERT) | 97.00 | A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding | |
| JointBERT-CAE | 97.0 | CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task | - |
| AGIF | 94.8 | AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling | |
| Stack-Propagation | 94.20 | A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding | |
| Context Encoder | 93.60 | Improving Slot Filling by Utilizing Contextual Information | - |
| SF-ID | 92.23 | A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling | |
| Slot-Gated BLSTM with Attension | 88.8 | Slot-Gated Modeling for Joint Slot Filling and Intent Prediction | - |
| Capsule-NLU | 0.918 | Joint Slot Filling and Intent Detection via Capsule Neural Networks | |
| DecomposedMetaSL | - | Decomposed Meta-Learning for Few-Shot Sequence Labeling | - |
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