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 326–344 of 344 papers

TitleStatusHype
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
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
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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