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

Showing 226250 of 344 papers

TitleStatusHype
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding0
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world NoiseCode0
Few-shot Intent Classification and Slot Filling with Retrieved Examples0
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible InputsCode1
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems0
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding0
Industry Scale Semi-Supervised Learning for Natural Language Understanding0
NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification0
Neural Data Augmentation via Example ExtrapolationCode0
Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR TranscriptCode1
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification0
A survey of joint intent detection and slot-filling models in natural language understanding0
A character representation enhanced on-device Intent Classification0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection0
Intent Classification and Slot Filling for Privacy PoliciesCode1
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning0
Deep Open Intent Classification with Adaptive Decision BoundaryCode1
Generation of complex database queries and API calls from natural language utterances0
Using multiple ASR hypotheses to boost i18n NLU performance0
Delexicalized Paraphrase Generation0
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 Systems0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention0
STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++Code0
Show:102550
← PrevPage 10 of 14Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TDT 0-8Accuracy (%)90.07Unverified
2Partially Fine-tuned HuBERTAccuracy (%)87.51Unverified
3Multi-SLURPAccuracy (%)78.33Unverified
4Finstreder (Conformer)Accuracy (%)53.11Unverified
5Finstreder (Quartznet)Accuracy (%)43.15Unverified
#ModelMetricClaimedVerifiedStatus
1mT5 Base (encoder-only)Intent Accuracy86.1Unverified
2mT5 Base (text-to-text)Intent Accuracy85.3Unverified
3XLM-R BaseIntent Accuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-wwm-ext-baseAccuracy85.5Unverified
#ModelMetricClaimedVerifiedStatus
1BERT (query + URL)F1-score0.77Unverified