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 251–275 of 344 papers

TitleStatusHype
arXivEdits: Understanding the Human Revision Process in Scientific Writing—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
A Single Example Can Improve Zero-Shot Data Generation—0
A Study on the Influence of Architecture Complexity of RNNs for Intent Classification in E-Commerce Chats in Bahasa Indonesia—0
A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog—0
A survey of joint intent detection and slot-filling models in natural language understanding—0
A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification—0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features—0
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR—0
Augmented Natural Language for Generative Sequence Labeling—0
Augmenting Task-Oriented Dialogue Systems with Relation Extraction—0
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling—0
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production—0
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling—0
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data—0
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque—0
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch—0
Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding—0
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders—0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots—0
Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network—0
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training—0
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems—0
Class Embeddings for Improved Out-of-Scope Detection in Intent Classification—0
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection—0
Show:102550
← PrevPage 11 of 14Next →
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