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Slot Filling On Mixsnips

Metrics

Micro F1

Results

Performance results of various models on this benchmark

Paper TitleRepository
BiSLU97.2Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
SLIM (PACL)96.8A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU-
SLIM96.5SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
Uni-MIS96.4Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction-
TFMN96.4A Transformer-based Threshold-Free Framework for Multi-Intent NLU-
TFMN (PACL)96.3A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU-
RoBERTa (PACL)96.2A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU-
DGIF95.9A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding-
SSRAN95.8A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding-
MISCA95.2MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
Co-guiding Net95.1Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
UGEN95.0Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling-
Global Intent-Slot Co-occurence95.0Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence-
GL-GIN94.9GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
AGIF94.5AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Topic Information94.4Exploiting Topic Information for Joint Intent Detection and Slot Filling-
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Slot Filling On Mixsnips | SOTA | HyperAI