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Intent Detection On Atis

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Accuracy
Paper TitleRepository
Slot-Gated BLSTM with Attension93.6Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
JointBERT-CAE97.5CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task
Stack-Propagation (+BERT)97.50A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
LIDSNet95.97LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network-
SF-ID (BLSTM) network97.76A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
CTRAN98.07CTRAN: CNN-Transformer-based Network for Natural Language Understanding
AGIF97.1AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Transformer-Capsule98.89Transformer-Capsule Model for Intent Detection-
Attention Encoder-Decoder NN98.43Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Joint model with recurrent slot label context98.40Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks-
Joint BERT + CRF97.9BERT for Joint Intent Classification and Slot Filling
Capsule-NLU0.95Joint Slot Filling and Intent Detection via Capsule Neural Networks
Bi-model with decoder98.99A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Joint BERT97.5BERT for Joint Intent Classification and Slot Filling
DELTA (BLSTM-CRF)97.40DELTA: A DEep learning based Language Technology plAtform
SF-ID97.76A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
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