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 101–150 of 344 papers

TitleStatusHype
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque—0
Integrating Regular Expressions with Neural Networks via DFA—0
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
DASB -- Discrete Audio and Speech Benchmark—0
Empirical Studies of Institutional Federated Learning For Natural Language Processing—0
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders—0
End to End Binarized Neural Networks for Text Classification—0
Augmented Natural Language for Generative Sequence Labeling—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English—0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots—0
In-Context Learning for Text Classification with Many Labels—0
Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques—0
Enhancing Pipeline-Based Conversational Agents with Large Language Models—0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
Ericson: An Interactive Open-Domain Conversational Search Agent—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems—0
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR—0
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss—0
A Single Example Can Improve Zero-Shot Data Generation—0
A character representation enhanced on-device Intent Classification—0
Cross-lingual intent classification in a low resource industrial setting—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses—0
CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education—0
Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification—0
Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context—0
A Comparison of LSTM and BERT for Small Corpus—0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features—0
Improving End-to-End Speech-to-Intent Classification with Reptile—0
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes—0
Improving the Intent Classification accuracy in Noisy Environment—0
Fuzzy Classification of Multi-intent Utterances—0
A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification—0
Improved intent classification based on context information using a windows-based approach—0
Forewords—0
Conversation Style Transfer using Few-Shot Learning—0
Generalized zero-shot audio-to-intent classification—0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents—0
Generation of complex database queries and API calls from natural language utterances—0
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach—0
Identifying Intention Posts in Discussion Forums—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—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