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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Jurnal Tekno Kompak Jurnal Abdimas Ilmiah Citra Bakti (JAICB)
Hendra Bunyamin
Maranatha Christian University
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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Journal : Journal of Information Technology and Computer Science

 1 
Utilizing Indonesian Universal Language Model Fine-tuning for Text Classification Bunyamin, Hendra
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1985.283 KB) | DOI: 10.25126/jitecs.202053215

Abstract

Inductive transfer learning technique has made a huge impact on the computer vision field. Particularly, computer vision  applications including object detection, classification, and segmentation, are rarely trained from scratch; instead, they are fine-tuned from pretrained models, which are products of learning from huge datasets. In contrast to computer vision, state-of-the-art natural language processing models are still generally trained from the ground up. Accordingly, this research attempts to investigate an adoption of the transfer learning technique for natural language processing. Specifically, we utilize a transfer learning technique called Universal Language Model Fine-tuning (ULMFiT) for doing an Indonesian news text classification task. The dataset for constructing the language model is collected from several news providers from January to December 2017 whereas the dataset employed for text classification task comes from news articles provided by the Agency for the Assessment and Application of Technology (BPPT). To examine the impact of ULMFiT, we provide a baseline that is a vanilla neural network with two hidden layers. Although the performance of ULMFiT on validation set is lower than the one of our baseline, we find that the benefits of ULMFiT for the classification task significantly reduce the overfitting, that is the difference between train and validation accuracies from 4% to nearly zero.
Co-Authors ADELIA Adelia Adelia, Adelia Andreas Widjaja Christian Wibisono Daniel Jahja Surjawan Diana Trivena Yulianti Doro Edi Hapnes Toba Johan, Meliana Christianti Juan Elisha Widyaya Julian, Matthew Julianti Kasih, Julianti Lucky Surya Haryadi Malendes, Mikhael Avner Maresha Caroline Wijanto Mewati Ayub Oscar Karnalim, Oscar Pangestu, Muftah Afrizal Risal Robby Tan Rossevine Artha Nathasya Teddy Marcus Zakaria Tjatur Kandaga Tomas Tunys Wasis, Galih Wicaksono Wenny Franciska Senjaya