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 301325 of 344 papers

TitleStatusHype
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features0
A Joint Learning Framework With BERT for Spoken Language Understanding0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems0
Metric Learning for Dynamic Text ClassificationCode0
Cross-lingual intent classification in a low resource industrial setting0
RNN based Incremental Online Spoken Language Understanding0
Iterative Delexicalization for Improved Spoken Language Understanding0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots0
Emu: Enhancing Multilingual Sentence Embeddings with Semantic SpecializationCode0
Real-world Conversational AI for Hotel Bookings0
Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning0
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog0
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data AugmentationCode0
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems0
Structural Scaffolds for Citation Intent Classification in Scientific PublicationsCode0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification0
Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue systemCode0
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems0
Intent Detection and Slots Prompt in a Closed-Domain Chatbot0
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context0
Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing0
Subword Semantic Hashing for Intent Classification on Small DatasetsCode0
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification0
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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