Intent Detection On Snips

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
CTRAN	99.42	CTRAN: CNN-Transformer-based Network for Natural Language Understanding
Stack-Propagation (+BERT)	99.0	A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
JointBERT-CAE	98.3	CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task
AGIF	98.1	AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
LIDSNet	98.0	LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network
Stack-Propagation	98.00	A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
SF-ID (BLSTM) network	97.43	A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
SF-ID	97.43	A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
Capsule-NLU	97.3	Joint Slot Filling and Intent Detection via Capsule Neural Networks
Slot-Gated BLSTM with Attension	97.00	Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

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Intent Detection On Snips | SOTA | HyperAI