Intent Detection
110 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
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Libraries
Use these libraries to find Intent Detection models and implementations
ATIS
SNIPS
BANKING77
CLINC150
Latest papers with no code
All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm
In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks
Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix.
Uncovering the Unseen: Discover Hidden Intentions by Micro-Behavior Graph Reasoning
HID presents a unique challenge in that hidden intentions lack the obvious visual representations to distinguish them from normal intentions.
Task Conditioned BERT for Joint Intent Detection and Slot-filling
Dialogue systems need to deal with the unpredictability of user intents to track dialogue state and the heterogeneity of slots to understand user preferences.
LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling
The model consists of an additional layer of latent dialogue action.
Utilisation of open intent recognition models for customer support intent detection
Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas.
A Comparative Analysis of Machine Learning Methods for Lane Change Intention Recognition Using Vehicle Trajectory Data
Accurately detecting and predicting lane change (LC)processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety.
Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems
This can lead to multiple intents or ambiguity in the input and output samples.
Multilingual Few-Shot Learning via Language Model Retrieval
Transformer-based language models have achieved remarkable success in few-shot in-context learning and drawn a lot of research interest.
MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup
To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup).
