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Found 14 papers, 5 papers with code
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount of labeled data.
New Intent Discovery with Pre-training and Contrastive Learning
Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate.
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations.
Effectiveness of Pre-training for Few-shot Intent Classification
This paper investigates the effectiveness of pre-training for few-shot intent classification.
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training
Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection.
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification
User intent classification plays a vital role in dialogue systems.
Reconstructing Capsule Networks for Zero-shot Intent Classification
With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification.
Adaptive Chemical Reaction Optimization for Global Numerical Optimization
A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains.
Chemical Reaction Optimization for the Set Covering Problem
The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications.
Evolutionary Artificial Neural Network Based on Chemical Reaction Optimization
Evolutionary algorithms (EAs) are very popular tools to design and evolve artificial neural networks (ANNs), especially to train them.
Real-Coded Chemical Reaction Optimization with Different Perturbation Functions
The distributions are tested by a set of well-known benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function.
Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using Chemical Reaction Optimization
An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid.
An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization
However, the functionality of the inter-molecular ineffective collision operator in the canonical CRO design overlaps that of the on-wall ineffective collision operator, which can potential impair the overall performance.
Sensor Deployment for Air Pollution Monitoring Using Public Transportation System
Air pollution monitoring is a very popular research topic and many monitoring systems have been developed.
