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All Journal International Journal of Engineering, Science and Information Technology Advance Sustainable Science, Engineering and Technology (ASSET)
Tri Lathif Mardi Suryanto
Universitas Negeri Malang
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Comparative Performance of Transformer Models for Cultural Heritage in NLP Tasks Tri Lathif Mardi Suryanto; Aji Prasetya Wibawa; Hariyono Hariyono; Andrew Nafalski
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1211

Abstract

AI and Machine Learning are crucial in advancing technology, especially for processing large, complex datasets. The transformer model, a primary approach in natural language processing (NLP), enables applications like translation, text summarization, and question-answer (QA) systems. This study compares two popular transformer models, FlanT5 and mT5, which are widely used yet often struggle to capture the specific context of the reference text. Using a unique Goddess Durga QA dataset with specialized cultural knowledge about Indonesia, this research tests how effectively each model can handle culturally specific QA tasks. The study involved data preparation, initial model training, ROUGE metric evaluation (ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum), and result analysis. Findings show that FlanT5 outperforms mT5 on multiple metrics, making it better at preserving cultural context. These results are impactful for NLP applications that rely on cultural insight, such as cultural preservation QA systems and context-based educational platforms.
Contextual Relevance-Driven Question Answering Generation: Experimental Insights Using Transformer-Based Models Tri Lathif Mardi Suryanto; Aji Prasetya Wibawa; Hariyono Hariyono; Hechmi Shili
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.989

Abstract

This study investigates the impact of contextual relevance and hyperparameter tuning on the performance of Transformer-based models in Question-Answer Generation (QAG). Utilising the FlanT5 model, experiments were conducted on a domain-specific dataset to assess how variations in learning rate and training epochs affect model accuracy and generalisation. Six QAG models were developed (QAG-A to QAG-F), each evaluated using ROUGE metrics to measure the quality of generated question-answer pairs. Results show that QAG-F and QAG-D achieved the highest performance, with QAG-F reaching a ROUGE-LSum of 0.4985. The findings highlight that careful tuning of learning rates and training duration significantly improves model performance, enabling more accurate and contextually appropriate question generation. Furthermore, the ability to generate both questions and answers from a single input enhances the interactivity and utility of NLP systems, particularly in knowledge-intensive domains. This study underscores the importance of contextual modelling and hyperparameter optimisation in generative NLP tasks, offering practical insights for improving chatbot development, educational tools, and digital heritage applications.
Co-Authors Aji Prasetya Wibawa Andrew Nafalski Hariyono Hariyono Hechmi SHILI