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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 344 papers

TitleStatusHype
Practical token pruning for foundation models in few-shot conversational virtual assistant systems—0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification—0
Privacy-preserving Representation Learning for Speech Understanding—0
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue—0
Prompt Perturbation Consistency Learning for Robust Language Models—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
PythonPal: Enhancing Online Programming Education through Chatbot-Driven Personalized Feedback—0
Quick Starting Dialog Systems with Paraphrase Generation—0
Real-world Conversational AI for Hotel Bookings—0
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey—0
Reliable and Interpretable Drift Detection in Streams of Short Texts—0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection—0
Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction—0
Robust Task Clustering for Deep Many-Task Learning—0
Scalable Semi-Supervised Query Classification Using Matrix Sketching—0
SciWING– A Software Toolkit for Scientific Document Processing—0
Semi-Supervised Few-Shot Intent Classification and Slot Filling—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems—0
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification—0
Single Example Can Improve Zero-Shot Data Generation—0
SNOiC: Soft Labeling and Noisy Mixup based Open Intent Classification Model—0
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution—0
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
TaDSE: Template-aware Dialogue Sentence Embeddings—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification—0
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding—0
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition—0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model—0
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text—0
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks—0
Towards Better Citation Intent Classification—0
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Training data reduction for multilingual Spoken Language Understanding systems—0
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information—0
User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario—0
User Intent Inference for Web Search and Conversational Agents—0
Using multiple ASR hypotheses to boost i18n NLU performance—0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding—0
Adapting Long Context NLM for ASR Rescoring in Conversational Agents—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
Why do you cite? An investigation on citation intents and decision-making classification processes—0
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
一种结合话语伪标签注意力的人机对话意图分类方法(A Human-machine Dialogue Intent Classification Method using Utterance Pseudo Label Attention)—0
Zero-Shot Learning for Joint Intent and Slot Labeling—0
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All datasetsSLURPMASSIVEKUAKE-QICORCAS-I

Benchmark Results

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