Abstract Natural Language Processing (NLP) is one of the most captivating applications of
Deep Learning. In this survey, we consider how the Data Augmentation training strategy can …

Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a
collection of 8 Korean natural language understanding (NLU) tasks, including Topic …

Shortcut Learning of Large Language Models in Natural Language Understanding Page 1 110
COMMUNICATIONS OF THE ACM | JANUARY 2024 | VOL. 67 | NO. 1 research IMA GE B Y …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Simple yet effective data augmentation techniques have been proposed for sentence-level
and sentence-pair natural language processing tasks. Inspired by these efforts, we design …

While there has been much recent work studying how linguistic information is encoded in
pre-trained sentence representations, comparatively little is understood about how these …

This position paper describes and critiques the Pretraining-Agnostic Identically Distributed
(PAID) evaluation paradigm, which has become a central tool for measuring progress in …

Recent work has shown that pre-trained language models such as BERT improve
robustness to spurious correlations in the dataset. Intrigued by these results, we find that the …