Intent Detection
109 papers with code • 17 benchmarks • 20 datasets
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
Benchmarks
These leaderboards are used to track progress in Intent Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Intent Detection models and implementations
ATIS
SNIPS
BANKING77
CLINC150
Latest papers with no code
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network
In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
Gaze-Guided Graph Neural Network for Action Anticipation Conditioned on Intention
We introduce the Gaze-guided Action Anticipation algorithm, which establishes a visual-semantic graph from the video input.
RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education
RECIPE4U includes comprehensive records of these interactions, including conversation logs, students' intent, students' self-rated satisfaction, and students' essay edit histories.
Automatic driving lane change safety prediction model based on LSTM
Autonomous driving technology can improve traffic safety and reduce traffic accidents.
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection
Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems.
Intelligent Mode-switching Framework for Teleoperation
User intention recognition is done at the operator side.
Designing deep neural networks for driver intention recognition
We explore a pre-defined search space for three deep neural network layer types that are capable to handle sequential data (a long-short term memory, temporal convolution, and a time-series transformer layer), and the influence of different data fusion strategies on the driver intention recognition performance.
ICL Markup: Structuring In-Context Learning using Soft-Token Tags
Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natural language.
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking
Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is impractical in data-limited domains.
ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic
This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain.
