HyperAI超神経

Intent Detection On Mixatis

評価指標

Accuracy

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
Accuracy
Paper TitleRepository
TFMN (PACL)82.9A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU-
RoBERTa (PACL)79.1A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU-
DGIF83.3A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding-
Co-guiding Net79.1Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
SLIM78.3SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
GL-GIN76.3GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
TFMN79.8A Transformer-based Threshold-Free Framework for Multi-Intent NLU-
Global Intent-Slot Co-occurence75.0Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
UGEN83.0Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Uni-MIS78.5Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction
MISCA76.7MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
Topic Information73.0Exploiting Topic Information for Joint Intent Detection and Slot Filling-
SLIM (PACL)81.9A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU-
SSRAN77.9A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding-
BiSLU81.5Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
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