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
Most implemented papers
MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic
In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future.
A Post-processing Method for Detecting Unknown Intent of Dialogue System via Pre-trained Deep Neural Network Classifier
In this paper, we propose SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers.
TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue
jasonwu0731/ToD-BERT
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EMNLP 2020
The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice.
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
LooperXX/AGIF
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Findings of the Association for Computational Linguistics 2020
Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction.
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving.
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification
fanolabs/0shot-classification
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ACL 2020
User intent classification plays a vital role in dialogue systems.
Privacy Guarantees for De-identifying Text Transformations
Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data.
Intent Detection with WikiHow
zharry29/wikihow-intent
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Asian Chapter of the Association for Computational Linguistics 2020
Modern task-oriented dialog systems need to reliably understand users' intents.
HINT3: Raising the bar for Intent Detection in the Wild
Intent Detection systems in the real world are exposed to complexities of imbalanced datasets containing varying perception of intent, unintended correlations and domain-specific aberrations.
