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 301–344 of 344 papers

TitleStatusHype
RNN based Incremental Online Spoken Language Understanding—0
Iterative Delexicalization for Improved Spoken Language Understanding—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents—0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots—0
Emu: Enhancing Multilingual Sentence Embeddings with Semantic SpecializationCode0
An Evaluation Dataset for Intent Classification and Out-of-Scope PredictionCode1
Real-world Conversational AI for Hotel Bookings—0
Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning—0
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data AugmentationCode0
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog—0
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems—0
Structural Scaffolds for Citation Intent Classification in Scientific PublicationsCode0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification—0
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 Systems—0
Benchmarking Natural Language Understanding Services for building Conversational AgentsCode1
BERT for Joint Intent Classification and Slot FillingCode1
Induction Networks for Few-Shot Text ClassificationCode1
Intent Detection and Slots Prompt in a Closed-Domain Chatbot—0
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context—0
Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing—0
Subword Semantic Hashing for Intent Classification on Small DatasetsCode0
Modeling Temporality of Human Intentions by Domain Adaptation—0
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification—0
DeepPavlov: Open-Source Library for Dialogue Systems—0
Multi-Layer Ensembling Techniques for Multilingual Intent Classification—0
Data Collection for Dialogue System: A Startup Perspective—0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques—0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa—0
Forewords—0
Open-Domain Neural Dialogue Systems—0
A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification—0
The First Evaluation of Chinese Human-Computer Dialogue TechnologyCode2
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks—0
Robust Task Clustering for Deep Many-Task Learning—0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario—0
Neural Graph Machines: Learning Neural Networks Using Graphs—0
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot FillingCode0
Scalable Semi-Supervised Query Classification Using Matrix Sketching—0
Identifying Intention Posts in Discussion Forums—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