SOTAVerified

Intent Detection

Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.

Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

Papers

Showing 5175 of 330 papers

TitleStatusHype
Efficient Intent Detection with Dual Sentence EncodersCode1
MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed TrafficCode1
BERT for Joint Intent Classification and Slot FillingCode1
Flippi: End To End GenAI Assistant for E-Commerce0
An Interdisciplinary Review of Commonsense Reasoning and Intent Detection0
Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation0
Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting FrameworkCode0
Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care0
Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation0
Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models0
Learning Multimodal AI Algorithms for Amplifying Limited User Input into High-dimensional Control SpaceCode0
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines0
Improving Generalization in Intent Detection: GRPO with Reward-Based Curriculum Sampling0
Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method0
Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making0
Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings0
Towards Refining Developer Questions using LLM-Based Named Entity Recognition for Developer Chatroom Conversations0
Intention Recognition in Real-Time Interactive Navigation MapsCode0
PSCon: Product Search Through ConversationsCode0
On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent DetectionCode0
Fitting Different Interactive Information: Joint Classification of Emotion and Intention0
Meta-Reflection: A Feedback-Free Reflection Learning Framework0
HiTZ at VarDial 2025 NorSID: Overcoming Data Scarcity with Language Transfer and Automatic Data Annotation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Bi-model with decoderAccuracy98.99Unverified
2Transformer-CapsuleAccuracy98.89Unverified
3Attention Encoder-Decoder NNAccuracy98.43Unverified
4Joint model with recurrent slot label contextAccuracy98.4Unverified
5CTRANAccuracy98.07Unverified
6Joint BERT + CRFAccuracy97.9Unverified
7SF-IDAccuracy97.76Unverified
8SF-ID (BLSTM) networkAccuracy97.76Unverified
9JointBERT-CAEAccuracy97.5Unverified
10Joint BERTAccuracy97.5Unverified
#ModelMetricClaimedVerifiedStatus
1SSRANAccuracy98.4Unverified
2BiSLUAccuracy97.8Unverified
3DGIFAccuracy97.8Unverified
4Co-guiding NetAccuracy97.7Unverified
5TFMNAccuracy97.7Unverified
6TFMN (PACL)Accuracy97.4Unverified
7MISCAAccuracy97.3Unverified
8Uni-MISAccuracy97.2Unverified
9SLIMAccuracy97.2Unverified
10UGENAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1DGIFAccuracy83.3Unverified
2UGENAccuracy83Unverified
3TFMN (PACL)Accuracy82.9Unverified
4SLIM (PACL)Accuracy81.9Unverified
5BiSLUAccuracy81.5Unverified
6TFMNAccuracy79.8Unverified
7RoBERTa (PACL)Accuracy79.1Unverified
8Co-guiding NetAccuracy79.1Unverified
9Uni-MISAccuracy78.5Unverified
10SLIMAccuracy78.3Unverified
#ModelMetricClaimedVerifiedStatus
1CTRANAccuracy99.42Unverified
2Stack-Propagation (+BERT)Accuracy99Unverified
3JointBERT-CAEAccuracy98.3Unverified
4AGIFAccuracy98.1Unverified
5LIDSNetAccuracy98Unverified
6Stack-PropagationAccuracy98Unverified
7SF-IDAccuracy97.43Unverified
8SF-ID (BLSTM) networkAccuracy97.43Unverified
9Capsule-NLUAccuracy97.3Unverified
10Slot-Gated BLSTM with AttensionAccuracy97Unverified
#ModelMetricClaimedVerifiedStatus
1plain-LSTMF10.89Unverified
2linear-NgramsF10.87Unverified
3glove-LSTMF10.86Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.42Unverified
2OCaTS (kNN-GPT-4)Accuracy (%)82.7Unverified
#ModelMetricClaimedVerifiedStatus
1JointBERT-CAEIntent Accuracy97.7Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)89.79Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)84.01Unverified
#ModelMetricClaimedVerifiedStatus
1CM-NetAcc94.56Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)97.12Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.84Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.62Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuracy94.27Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.57Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)87.41Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)82.45Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuarcy85.02Unverified
#ModelMetricClaimedVerifiedStatus
1General SLU Model w/ ProfileAccuracy0.85Unverified