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 276–300 of 344 papers

TitleStatusHype
Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition—0
Controlled Data Generation via Insertion Operations for NLU—0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents—0
Conversation Style Transfer using Few-Shot Learning—0
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach—0
CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
Cross-lingual intent classification in a low resource industrial setting—0
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss—0
DASB -- Discrete Audio and Speech Benchmark—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
Data Augmentation for Intent Classification—0
Data Augmentation for Intent Classification of German Conversational Agents in the Finance Domain—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
Data balancing for boosting performance of low-frequency classes in Spoken Language Understanding—0
Data Collection for Dialogue System: A Startup Perspective—0
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems—0
Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data—0
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling—0
DeepPavlov: Open-Source Library for Dialogue Systems—0
Delexicalized Paraphrase Generation—0
Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing—0
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification—0
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection—0
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model—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