Although deep learning breakthroughs in NLP are based on learning distributed word
representations by neural language models, these methods suffer from a classic drawback …

Tokenization is a significant primary step for the training of the Pre-trained Language Model
(PLM), which alleviates the challenging Out-of-Vocabulary problem in the area of Natural …

Although deep neural networks have recently led to great achievements in machine
translation (MT), various challenges are still encountered during the development of Korean …

Various research approaches have attempted to solve the length difference problem
between the surface form and the base form of words in the Korean morphological analysis …

Korean morphological analysis has been considered as a sequence of morpheme
processing and POS tagging. Thus, a pipeline model of the tasks has been adopted widely …

Korean morphological variations present unique opportunities and challenges in natural
language processing (NLP), necessitating an advanced understanding of morpheme-based …

Two major problems of Korean part-of-speech (POS) tagging are that the word-spacing unit
is not mapped one-to-one to a POS tag and that morphemes should be recovered during …

We apply convolutional neural networks to the task of shallow morpheme segmentation
using low-resource datasets for 5 different languages. We show that both in fully supervised …

In this paper, we present an FST based approach for conducting morphological analysis,
lemmatization and generation of Lushootseed words. Furthermore, we use the FST to …

Machine translation (MT) has recently attracted much research on various advanced
techniques (ie, statistical-based and deep learning-based) and achieved great results for …