Intent Detection On Mixatis

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
DGIF	83.3	A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
UGEN	83.0	Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
TFMN (PACL)	82.9	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
SLIM (PACL)	81.9	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
BiSLU	81.5	Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
TFMN	79.8	A Transformer-based Threshold-Free Framework for Multi-Intent NLU
RoBERTa (PACL)	79.1	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
Co-guiding Net	79.1	Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
Uni-MIS	78.5	Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction
SLIM	78.3	SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
SSRAN	77.9	A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
MISCA	76.7	MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
GL-GIN	76.3	GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Global Intent-Slot Co-occurence	75.0	Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
Topic Information	73.0	Exploiting Topic Information for Joint Intent Detection and Slot Filling

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Intent Detection On Mixatis | SOTA | HyperAI