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 251–275 of 344 papers

TitleStatusHype
NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu—0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification—0
Neural Data Augmentation via Example ExtrapolationCode0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
A survey of joint intent detection and slot-filling models in natural language understanding—0
A character representation enhanced on-device Intent Classification—0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Generation of complex database queries and API calls from natural language utterances—0
Using multiple ASR hypotheses to boost i18n NLU performance—0
Delexicalized Paraphrase Generation—0
Attentively Embracing Noise for Robust Latent Representation in BERTCode0
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems—0
STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++Code0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
Meta learning to classify intent and slot labels with noisy few shot examples—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey—0
Empirical Studies of Institutional Federated Learning For Natural Language Processing—0
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness—0
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes—0
SciWING– A Software Toolkit for Scientific Document Processing—0
End to End Binarized Neural Networks for Text Classification—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
STIL -- Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++Code0
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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