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Emmanuella Anggi Siallagan
Faculty of Computer Science Universitas Indonesia
Computer Science & IT Library & Information Science

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Poetry Generation for Indonesian Pantun: Comparison Between SeqGAN and GPT-2 Emmanuella Anggi Siallagan; Ika Alfina
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1113

Abstract

Pantun is a traditional Malay poem consisting of four lines: two lines of deliverance and two lines of messages. Each ending-line word in pantun forms an ABAB rhyme pattern. In this work, we compare the performance of Sequence Generative Adversarial Nets (SeqGAN) and Generative Pre-trained Transformer 2 (GPT-2) in automatically generating Indonesian pantun. We also created the first publicly available Indonesian pantun dataset that consists of 7.8K pantun. We evaluated how well each model produced pantun by its lexical richness and its formedness. We introduced the evaluation of pantun with two aspects: structure and rhyme. GPT-2 performs better with a margin of 29.40% than SeqGAN in forming the structure, 35.20% better in making rhyming patterns, and 0.04 difference in giving richer vocabulary to its generated pantun.
Co-Authors Alfina, Ika