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

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Showing 51–100 of 344 papers

TitleStatusHype
PythonPal: Enhancing Online Programming Education through Chatbot-Driven Personalized Feedback—0
TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data ConsistencyCode0
A Preliminary Exploration with GPT-4o Voice Mode—0
Exploring Robustness of Multilingual LLMs on Real-World Noisy DataCode0
Joint Automatic Speech Recognition And Structure Learning For Better Speech UnderstandingCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification—0
Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision BoundaryCode0
Intent-Aware Dialogue Generation and Multi-Task Contrastive Learning for Multi-Turn Intent Classification—0
Predicting User Intents and Musical Attributes from Music Discovery ConversationsCode0
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production—0
Improved intent classification based on context information using a windows-based approach—0
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch—0
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasksCode0
Intent Classification for Bank Chatbots through LLM Fine-Tuning—0
Efficacy of Synthetic Data as a Benchmark—0
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection—0
Uddessho: An Extensive Benchmark Dataset for Multimodal Author Intent Classification in Low-Resource Bangla LanguageCode0
LLM-based Weak Supervision Framework for Query Intent Classification in Video Search—0
LLMs Will Always Hallucinate, and We Need to Live With This—0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
Practical token pruning for foundation models in few-shot conversational virtual assistant systems—0
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce—0
Exploring Description-Augmented Dataless Intent ClassificationCode0
Why do you cite? An investigation on citation intents and decision-making classification processes—0
One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive LearningCode0
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors—0
DASB -- Discrete Audio and Speech Benchmark—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Self-Supervised Speech Representations are More Phonetic than SemanticCode0
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic SpeakersCode0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
Luganda Speech Intent Recognition for IoT Applications—0
OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs—0
CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education—0
New Semantic Task for the French Spoken Language Understanding MEDIA BenchmarkCode0
Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition—0
LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification—0
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent ClassificationCode0
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders—0
Augmenting Automation: Intent-Based User Instruction Classification with Machine LearningCode0
Prompt Perturbation Consistency Learning for Robust Language Models—0
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization—0
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks—0
PerSHOP -- A Persian dataset for shopping dialogue systems modeling—0
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System—0
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling—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