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
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
一种结合话语伪标签注意力的人机对话意图分类方法(A Human-machine Dialogue Intent Classification Method using Utterance Pseudo Label Attention)—0
Zero-Shot Learning for Joint Intent and Slot Labeling—0
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce—0
Zero-Shot Learning with Common Sense Knowledge Graphs—0
A character representation enhanced on-device Intent Classification—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
A Comparison of LSTM and BERT for Small Corpus—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—0
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification—0
A Financial Service Chatbot based on Deep Bidirectional Transformers—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
A Joint Learning Framework With BERT for Spoken Language Understanding—0
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems—0
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications—0
A Preliminary Exploration with GPT-4o Voice Mode—0
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection—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