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 76–100 of 344 papers

TitleStatusHype
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production—0
A Financial Service Chatbot based on Deep Bidirectional Transformers—0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English—0
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
Data Augmentation for Intent Classification of German Conversational Agents in the Finance Domain—0
Augmenting Task-Oriented Dialogue Systems with Relation Extraction—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
Data Augmentation for Intent Classification—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
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
DASB -- Discrete Audio and Speech Benchmark—0
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications—0
Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing—0
Augmented Natural Language for Generative Sequence Labeling—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
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—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