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 201–250 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
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
Show:102550
← PrevPage 5 of 7Next →
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