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 201–225 of 344 papers

TitleStatusHype
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce—0
Few-shot Intent Classification and Slot Filling with Retrieved Examples—0
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Fine-grained Intent Classification in the Legal Domain—0
Forewords—0
Fuzzy Classification of Multi-intent Utterances—0
Generalized zero-shot audio-to-intent classification—0
Generation of complex database queries and API calls from natural language utterances—0
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Identifying Intention Posts in Discussion Forums—0
IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection—0
Improved intent classification based on context information using a windows-based approach—0
Improved Text Classification via Contrastive Adversarial Training—0
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis—0
Improving End-to-End Speech-to-Intent Classification with Reptile—0
Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context—0
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes—0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses—0
Improving the Intent Classification accuracy in Noisy Environment—0
In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification—0
In-Context Learning for Text Classification with Many Labels—0
RNN based Incremental Online Spoken Language Understanding—0
Industry Scale Semi-Supervised Learning for Natural Language Understanding—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