SOTAVerified

Intent Classification

Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents

Source: Multi-Layer Ensembling Techniques for Multilingual Intent Classification

Papers

Showing 5175 of 344 papers

TitleStatusHype
Intent Classification in Question-Answering Using LSTM ArchitecturesCode0
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling TaskCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
The Open-World Lottery Ticket Hypothesis for OOD Intent ClassificationCode0
ImpactCite: An XLNet-based method for Citation Impact AnalysisCode0
Joint Automatic Speech Recognition And Structure Learning For Better Speech UnderstandingCode0
Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision BoundaryCode0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
Bengali Intent Classification with Generative Adversarial BERTCode0
Generalized Intent Discovery: Learning from Open World Dialogue SystemCode0
Benchmarking Language-agnostic Intent Classification for Virtual Assistant PlatformsCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent ClassificationCode0
Fast Intent Classification for Spoken Language UnderstandingCode0
Augmenting Automation: Intent-Based User Instruction Classification with Machine LearningCode0
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasksCode0
Exploring Robustness of Multilingual LLMs on Real-World Noisy DataCode0
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelsCode0
Out-of-Scope Domain and Intent Classification through Hierarchical Joint ModelingCode0
Attentively Embracing Noise for Robust Latent Representation in BERTCode0
End-to-end Spoken Language Understanding with Tree-constrained Pointer GeneratorCode0
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot FillingCode0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Show:102550
← PrevPage 3 of 14Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TDT 0-8Accuracy (%)90.07Unverified
2Partially Fine-tuned HuBERTAccuracy (%)87.51Unverified
3Multi-SLURPAccuracy (%)78.33Unverified
4Finstreder (Conformer)Accuracy (%)53.11Unverified
5Finstreder (Quartznet)Accuracy (%)43.15Unverified
#ModelMetricClaimedVerifiedStatus
1mT5 Base (encoder-only)Intent Accuracy86.1Unverified
2mT5 Base (text-to-text)Intent Accuracy85.3Unverified
3XLM-R BaseIntent Accuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-wwm-ext-baseAccuracy85.5Unverified
#ModelMetricClaimedVerifiedStatus
1BERT (query + URL)F1-score0.77Unverified