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 …

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

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 …

This paper focuses on the Data Augmentation for low-resource Natural Language
Understanding (NLU) tasks. We propose Prompt-based D} ata Augmentation model …

In literature, the machine learning-based studies of sentiment analysis are usually
supervised learning which must have pre-labeled datasets to be large enough in certain …

We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice
questions across 45 subjects ranging from humanities to STEM. While prior Korean …

Recently, neural machine translation (NMT) has been extended to multilinguality, that is to
handle more than one translation direction with a single system. Multilingual NMT showed …

This paper explores new frontiers in agricultural natural language processing (NLP) by
investigating the effectiveness of food-related text corpora for pretraining transformer-based …

Pretraining domain-specific language models remains an important challenge which limits
their applicability in various areas such as agriculture. This paper investigates the …