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 1–10 of 344 papers

TitleStatusHype
Leveraging GANs for citation intent classification and its impact on citation network analysisCode0
Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction—0
Intent Classification on Low-Resource Languages with Query Similarity Search—0
In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach—0
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
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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