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Exploring Foreign Language Students’ Perceptions of the Guided Use of Machine Translation (GUMT) Model for Korean Writing
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In: L2 Journal, vol 14, iss 1 (2022)
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| 2 |
On the characteristic of personal reference terms in Korean: A comparison with Japanese based on TV dramas
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In: Conference Proceedings for the 9th Korean Studies Association of Australia (KSAA) Biennial Conference 2015 (2022)
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A Proposed Resolution to the Problem of Geographical Inversion in Japanese Language Origins
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| 6 |
Intoxication and pitch control in tonal and non-tonal language speakers ...
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| 9 |
Heritage Language Development and Maintenance of Heritage Speakers of Korean in Australia in Primary School Years ...
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| 10 |
Crosslinguistic Influence in the Discrimination of Korean Stop Contrast by Heritage Speakers and Second Language Learners
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In: Languages; Volume 7; Issue 1; Pages: 6 (2022)
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| 11 |
Text Data Augmentation for the Korean Language
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3425 (2022)
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Abstract:
Data augmentation (DA) is a universal technique to reduce overfitting and improve the robustness of machine learning models by increasing the quantity and variety of the training dataset. Although data augmentation is essential in vision tasks, it is rarely applied to text datasets since it is less straightforward. Some studies have concerned text data augmentation, but most of them are for the majority languages, such as English or French. There have been only a few studies on data augmentation for minority languages, e.g., Korean. This study fills the gap by demonstrating several common data augmentation methods and Korean corpora with pre-trained language models. In short, we evaluate the performance of two text data augmentation approaches, known as text transformation and back translation. We compare these augmentations among Korean corpora on four downstream tasks: semantic textual similarity (STS), natural language inference (NLI), question duplication verification (QDV), and sentiment classification (STC). Compared to cases without augmentation, the performance gains when applying text data augmentation are 2.24%, 2.19%, 0.66%, and 0.08% on the STS, NLI, QDV, and STC tasks, respectively.
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Keyword:
data augmentation; Korean language processing; language modeling
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URL: https://doi.org/10.3390/app12073425
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| 12 |
Bilingual, Intergenerational Worship and Ministry for Unity
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In: Religions; Volume 13; Issue 4; Pages: 287 (2022)
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| 13 |
Ghost in the Kitchen: Multiracial Korean Americans (Re)Defining Cultural Authenticity
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In: Genealogy; Volume 6; Issue 2; Pages: 35 (2022)
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| 14 |
訓民正音創製と仏教政策 ; The creation of Hunminjeongeum and Buddhism policy
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| 20 |
Are you a die-hard K-pop fan? Examining English Korean code mixing uttered by an American native speaker youtuber
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In: Journal of Applied Linguistics and Literature, Vol 7, Iss 1, Pp 15-33 (2022) (2022)
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