Slot Filling On Mixsnips

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Micro F1

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
BiSLU	97.2	Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
SLIM (PACL)	96.8	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
SLIM	96.5	SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
Uni-MIS	96.4	Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction
TFMN	96.4	A Transformer-based Threshold-Free Framework for Multi-Intent NLU
TFMN (PACL)	96.3	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
RoBERTa (PACL)	96.2	A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
DGIF	95.9	A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
SSRAN	95.8	A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
MISCA	95.2	MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
Co-guiding Net	95.1	Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
UGEN	95.0	Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Global Intent-Slot Co-occurence	95.0	Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
GL-GIN	94.9	GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
AGIF	94.5	AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Topic Information	94.4	Exploiting Topic Information for Joint Intent Detection and Slot Filling

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