Intent Detection On Mixsnips

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
SSRAN	98.4	A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
DGIF	97.8	A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
BiSLU	97.8	Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
Co-guiding Net	97.7	Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
TFMN	97.7	A Transformer-based Threshold-Free Framework for Multi-Intent NLU
TFMN (PACL)	97.4	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
MISCA	97.3	MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
SLIM	97.2	SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
Uni-MIS	97.2	Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction
SLIM (PACL)	96.9	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
UGEN	96.9	Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
RoBERTa (PACL)	96.5	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
AGIF	96.5	AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Topic Information	96.3	Exploiting Topic Information for Joint Intent Detection and Slot Filling
GL-GIN	95.6	GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Global Intent-Slot Co-occurence	95.5	Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence

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Intent Detection On Mixsnips | SOTA | HyperAI