Learning from few examples: A summary of approaches to few-shot learning
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …
from a few training samples. Requiring a large number of data samples, many deep learning …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
Rlprompt: Optimizing discrete text prompts with reinforcement learning
Prompting has shown impressive success in enabling large pretrained language models
(LMs) to perform diverse NLP tasks, especially when only few downstream data are …
(LMs) to perform diverse NLP tasks, especially when only few downstream data are …
A survey on recent approaches for natural language processing in low-resource scenarios
Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …
language applications. As they are known for requiring large amounts of training data, there …
Generalizing from a few examples: A survey on few-shot learning
Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …
Differentiable prompt makes pre-trained language models better few-shot learners
Large-scale pre-trained language models have contributed significantly to natural language
processing by demonstrating remarkable abilities as few-shot learners. However, their …
processing by demonstrating remarkable abilities as few-shot learners. However, their …
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000
sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The …
sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The …
Transferable multi-domain state generator for task-oriented dialogue systems
Over-dependence on domain ontology and lack of knowledge sharing across domains are
two practical and yet less studied problems of dialogue state tracking. Existing approaches …
two practical and yet less studied problems of dialogue state tracking. Existing approaches …
[HTML][HTML] Few-shot learning for medical text: A review of advances, trends, and opportunities
Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …
small numbers of labeled instances for training. With many medical topics having limited …
Hybrid attention-based prototypical networks for noisy few-shot relation classification
The existing methods for relation classification (RC) primarily rely on distant supervision
(DS) because large-scale supervised training datasets are not readily available. Although …
(DS) because large-scale supervised training datasets are not readily available. Although …