Slot Filling On Mixatis

Metrics

Micro F1

Results

Performance results of various models on this benchmark

		Paper Title
MISCA	90.5	MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
Co-guiding Net	89.8	Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
SSRAN	89.4	A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
BiSLU	89.4	Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
UGEN	89.2	Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Topic Information	88.7	Exploiting Topic Information for Joint Intent Detection and Slot Filling
DGIF	88.5	A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
Global Intent-Slot Co-occurence	88.5	Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
SLIM	88.5	SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
Uni-MIS	88.3	Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction
GL-GIN	88.3	GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
TFMN	88.0	A Transformer-based Threshold-Free Framework for Multi-Intent NLU
SLIM (PACL)	87.3	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
TFMN (PACL)	86.7	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
RoBERTa (PACL)	86.0	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU

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Slot Filling On Mixatis | SOTA | HyperAI