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 301–325 of 344 papers

TitleStatusHype
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification—0
Efficacy of Synthetic Data as a Benchmark—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Emora: An Inquisitive Social Chatbot Who Cares For You—0
Empirical Studies of Institutional Federated Learning For Natural Language Processing—0
End to End Binarized Neural Networks for Text Classification—0
End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents—0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English—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
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification—0
Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks—0
Exploring Zero and Few-shot Techniques for Intent Classification—0
Few-shot Intent Classification and Slot Filling with Retrieved Examples—0
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Fine-grained Intent Classification in the Legal Domain—0
Forewords—0
Fuzzy Classification of Multi-intent Utterances—0
Generalized zero-shot audio-to-intent classification—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