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Comparative Study of Multiclass Text Classification in Research Proposals Using Pretrained Language Models
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4522 (2022)
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Abstract:
Recently, transformer-based pretrained language models have demonstrated stellar performance in natural language understanding (NLU) tasks. For example, bidirectional encoder representations from transformers (BERT) have achieved outstanding performance through masked self-supervised pretraining and transformer-based modeling. However, the original BERT may only be effective for English-based NLU tasks, whereas its effectiveness for other languages such as Korean is limited. Thus, the applicability of BERT-based language models pretrained in languages other than English to NLU tasks based on those languages must be investigated. In this study, we comparatively evaluated seven BERT-based pretrained language models and their expected applicability to Korean NLU tasks. We used the climate technology dataset, which is a Korean-based large text classification dataset, in research proposals involving 45 classes. We found that the BERT-based model pretrained on the most recent Korean corpus performed the best in terms of Korean-based multiclass text classification. This suggests the necessity of optimal pretraining for specific NLU tasks, particularly those in languages other than English.
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Keyword:
bidirectional encoder representations from transformers; cross-lingual representation learning; multiclass text classification; multilingual representation learning; natural language understanding; transfer learning
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URL: https://doi.org/10.3390/app12094522
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62 |
Effects of Japanese Special Moras Education Using Evernote
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In: Education Sciences; Volume 12; Issue 4; Pages: 270 (2022)
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63 |
Object Coreference in German: The Reflexive sich as a Problem for Derivational Approaches to Binding
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In: Philosophies; Volume 7; Issue 1; Pages: 5 (2022)
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64 |
Mapping Enabling Conditions for High-Quality PBL: A Collaboratory Approach
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In: Education Sciences; Volume 12; Issue 3; Pages: 222 (2022)
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65 |
Exploring Measurement through Coding: Children’s Conceptions of a Dynamic Linear Unit with Robot Coding Toys
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In: Education Sciences; Volume 12; Issue 2; Pages: 143 (2022)
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66 |
Causal Emergence: When Distortions in a Map Obscure the Territory
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In: Philosophies; Volume 7; Issue 2; Pages: 30 (2022)
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67 |
The Association between Mothers’ Smartphone Dependency and Preschoolers’ Problem Behavior and Emotional Intelligence
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In: Healthcare; Volume 10; Issue 2; Pages: 185 (2022)
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68 |
CFPR: interview avec FRA2019_0033
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In: https://nakala.fr/10.34847/nkl.dc77f1k4#3ed5f837b79f396d7718191182bb16955ef1a48b (2022)
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69 |
Towards a Socio-technical System of Culturally Responsive Teaching: The Interplay between Individuals, Communities and Resources
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72 |
Morphological Paradigms: Computational Structure and Unsupervised Learning ...
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73 |
Additional file 1 of Discomfort, pain and stiffness: what do these terms mean to patients? A cross-sectional survey with lexical and qualitative analyses ...
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Additional file 1 of Discomfort, pain and stiffness: what do these terms mean to patients? A cross-sectional survey with lexical and qualitative analyses ...
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Discomfort, pain and stiffness: what do these terms mean to patients? A cross-sectional survey with lexical and qualitative analyses ...
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76 |
Discomfort, pain and stiffness: what do these terms mean to patients? A cross-sectional survey with lexical and qualitative analyses ...
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sj-docx-1-jls-10.1177_0261927X221092098 - Supplemental material for Willingness to Communicate and its High-Evidence Factors: A Meta-Analytic Structural Equation Modeling Approach ...
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78 |
Willingness to Communicate and its High-Evidence Factors: A Meta-Analytic Structural Equation Modeling Approach ...
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sj-docx-1-jls-10.1177_0261927X221092098 - Supplemental material for Willingness to Communicate and its High-Evidence Factors: A Meta-Analytic Structural Equation Modeling Approach ...
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Willingness to Communicate and its High-Evidence Factors: A Meta-Analytic Structural Equation Modeling Approach ...
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