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 226–250 of 344 papers

TitleStatusHype
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
Integrating Regular Expressions with Neural Networks via DFA—0
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding—0
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery—0
Intent-Aware Dialogue Generation and Multi-Task Contrastive Learning for Multi-Turn Intent Classification—0
Intent Classification for Bank Chatbots through LLM Fine-Tuning—0
Intent Classification on Low-Resource Languages with Query Similarity Search—0
Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages—0
Intent Detection and Slots Prompt in a Closed-Domain Chatbot—0
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems—0
Introducing Semantics into Speech Encoders—0
Iterative Delexicalization for Improved Spoken Language Understanding—0
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness—0
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks—0
Joint model for intent and entity recognition—0
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors—0
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog—0
Joint Text and Label Generation for Spoken Language Understanding—0
KBot: a Knowledge graph based chatBot for natural language understanding over linked data—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains—0
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks—0
Label Errors in BANKING77—0
LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification—0
Learning Better Intent Representations for Financial Open 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