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 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
1mT5 Base (encoder-only)Intent Accuracy86.1—Unverified
2mT5 Base (text-to-text)Intent Accuracy85.3—Unverified
3XLM-R BaseIntent Accuracy85.1—Unverified