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 125 of 330 papers

TitleStatusHype
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
Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on Norwegian Dialectal Slot and Intent DetectionCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
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
Evaluating Pixel Language Models on Non-Standardized Languages0
Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning0
Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning0
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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-ID (BLSTM) networkAccuracy97.76Unverified
8SF-IDAccuracy97.76Unverified
9JointBERT-CAEAccuracy97.5Unverified
10Stack-Propagation (+BERT)Accuracy97.5Unverified
#ModelMetricClaimedVerifiedStatus
1SSRANAccuracy98.4Unverified
2BiSLUAccuracy97.8Unverified
3DGIFAccuracy97.8Unverified
4TFMNAccuracy97.7Unverified
5Co-guiding NetAccuracy97.7Unverified
6TFMN (PACL)Accuracy97.4Unverified
7MISCAAccuracy97.3Unverified
8Uni-MISAccuracy97.2Unverified
9SLIMAccuracy97.2Unverified
10SLIM (PACL)Accuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1DGIFAccuracy83.3Unverified
2UGENAccuracy83Unverified
3TFMN (PACL)Accuracy82.9Unverified
4SLIM (PACL)Accuracy81.9Unverified
5BiSLUAccuracy81.5Unverified
6TFMNAccuracy79.8Unverified
7Co-guiding NetAccuracy79.1Unverified
8RoBERTa (PACL)Accuracy79.1Unverified
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