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Intent Detection On Snips

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Accuracy
Paper TitleRepository
AGIF98.1AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
LIDSNet98.0LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network-
SF-ID (BLSTM) network97.43A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
Stack-Propagation (+BERT)99.0A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
Capsule-NLU97.3Joint Slot Filling and Intent Detection via Capsule Neural Networks
Stack-Propagation98.00A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
JointBERT-CAE98.3CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task
SF-ID97.43A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
Slot-Gated BLSTM with Attension97.00Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
CTRAN99.42CTRAN: CNN-Transformer-based Network for Natural Language Understanding
0 of 10 row(s) selected.
Intent Detection On Snips | SOTA | HyperAI