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

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

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 …

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 …

This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the
performance on text classification tasks. AEDA includes only random insertion of …

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

Data augmentation is an effective solution to data scarcity in low-resource scenarios.
However, when applied to token-level tasks such as NER, data augmentation methods often …

Abstract Named Entity Recognition (NER) for low-resource languages is a both practical and
challenging research problem. This paper addresses zero-shot transfer for cross-lingual …

As more and more Arabic texts emerged on the Internet, extracting important information
from these Arabic texts is especially useful. As a fundamental technology, Named entity …

We propose a method to recommend music for an input video while allowing a user to guide
music selection with free-form natural language. A key challenge of this problem setting is …