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 276300 of 344 papers

TitleStatusHype
A Study on the Influence of Architecture Complexity of RNNs for Intent Classification in E-Commerce Chats in Bahasa Indonesia0
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent ClassificationCode1
IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection0
Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network0
User Intent Inference for Web Search and Conversational Agents0
Learning with Weak Supervision for Email Intent Detection0
ImpactCite: An XLNet-based method for Citation Impact AnalysisCode0
Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data0
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque0
MTSI-BERT: A Session-aware Knowledge-based Conversational AgentCode1
End-to-End Slot Alignment and Recognition for Cross-Lingual NLUCode1
Learning to Classify Intents and Slot Labels Given a Handful of Examples0
A Financial Service Chatbot based on Deep Bidirectional Transformers0
Intent Classification in Question-Answering Using LSTM ArchitecturesCode0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses0
Stacked DeBERT: All Attention in Incomplete Data for Text ClassificationCode1
Fast Intent Classification for Spoken Language UnderstandingCode0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features0
A Joint Learning Framework With BERT for Spoken Language Understanding0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems0
ConveRT: Efficient and Accurate Conversational Representations from TransformersCode1
Interactive Classification by Asking Informative QuestionsCode1
Metric Learning for Dynamic Text ClassificationCode0
Cross-lingual intent classification in a low resource industrial setting0
Reconstructing Capsule Networks for Zero-shot Intent ClassificationCode1
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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