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Journal : scientific journal of informatics

Combining Multiple Text Representations for Improved Automatic Evaluation of Indonesian Essay Answers Wibowo, Moh Edi; Rokhman, Nur; Sihabudin, Agus
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.9440

Abstract

multiple-choices, regarding students’ learning achievement. When the number of students in a class is huge, however, examinations using essay questions become hard to conduct and take a long evaluation time. Automatic essay evaluation has, therefore, become a potential approach in this situation. Various methods have been proposed, however, optimal solutions for such evaluation in the Indonesian language are less known. Furthermore, with the rapid development of machine learning approaches, in particular deep learning approaches, the investigation of such optimal solutions becomes more necessary. Method: To address the aforementioned issue, this study proposed the investigation of text representation approaches for optimal automatic evaluation of Indonesian essay answers. The investigation compared pre-trained word embedding methods such as Word2vec, GloVe, FastText, and RoBERTa, as well as compared text encoding methods such as long short-term memories (LSTMs) and transformers. LSTMs are able to capture temporal semantics by employing state variables, while transformers are able to capture long-term dependency between parts of their input sequences. Additionally, we investigated classification-based and similarity-based training to build text encoders. We expected that these training approaches allowed encoders to extract different views of information. We compared classification results produced by different text encoders and combinations of text encoders. Result: We evaluated various text representation approaches using the UKARA dataset. Our experiments showed that the FastText word embedding method outperformed the Word2vec, GloVe, and RoBERTa methods. The FastText method achieved an F1-score of 75.43% on validation sets, while the Word2vec, GloVe, and RoBERTa methods achieved F1-scores of 69.56%, 74.53%, and 72.87%, respectively. In addition, the experiments showed that combinations of text encoders outperformed individual encoders. The combination of the LSTM encoder, the transformer encoder, and the TF-IDF encoder obtained an F1-score of 77.22% in the best case, which is better than the best F1-scores of the individual LSTM encoders (75.35%), the best combination of transformer encoders (71.49%), and the individual TF-IDF encoder (76.69%). We observed that LSTM encoders produced better performance when they were built using classification-based training. Meanwhile, the transformer encoders obtained better performance when built using similarity-based training. Novelty: The novelty proposed in this research is the optimal combination of text encoders specifically constructed for the evaluation of essay answers in the Indonesian language. Our experiments showed that the combination of three encoders - namely the LSTM encoder built using classification-based training, the transformer encoder built using classification-based and similarity-based training, and the TF-IDF encoder - obtained the best classification performance.
Co-Authors Abdul Khafid, Abdul Agus Priyadi Agus Priyadi Agus Setiawan Agus Sihabuddin Ahmad Riyadi Akbar, Muhamat Maulana Akrom, Ahmad Alamsyah, Reka Andleeb, Naima Andreas Heri Kurniawan Andriyani, Linia Riski Angga Eko Pramono Anifudin Azis Arga adriansah Arif Sutrisno Bambang Widjanarko Susilo Bayu Aryanto Belo, Teresa Freitas Cahaya Jatmoko Cahyaning, Enggar Kartika Chasandra Puspitasari Choirunnisa, Ayu Rizka Citra Nova Amalia Delinda Dalis Yuliani Dewi, Fadila Ayu Kumala Dini Kharisatul Khabibah Dwi Nugroho Dwikinta, Made Anindya Dyah Ari Susanti Edi Sugiarto Edwin Zusrony Eka Sudarusman, Eka ENI ENDARYATI Eni Endaryati Fadilla, Syakira Fangela Myas Sari Fikri Budiman Fitro Nur Hakim Grace, Flora Helmi Indra Fauzi Heru Lestiawan Heryawan, Lukman Indonesia, Dini Kharisatul Khabibah Iqnatius Dimas Nugroho Irda Yunianto Iswantoro, Ibnu Yanuar Jamaludin, Haris Jatmiko, Cahaya Junta Zeniarja Juwita Maharanti Kirana, Kusuma Candra Kurniawan, Andreas Heri Kusuma Chandra Kirana, Kusuma Chandra Kusumajaya, Robby Andika Kusumo, Haryo Laksana, Deddy Award Widya Luthfia Nurma Hapsari Maharani Maharani, Maharani Miftah, Mohamad Miftakhur Rohmah, Miftakhur Muinah Fadhilah, Muinah Muljono Muljono Murdayana, Murdayana Murdianingsih, Dian Nining Fitriani Nur Chakiki Ramadiansyah Nurgiyantoro, Edi Nuryati Nuryati Permata, Aurel Priastyca Praretno Wibowo Pratiwi, Julia Dian Purnasari, Eni Puspita, Kori Puspitasari, Arnetta Aulia Puspitasari, Chasandra Qayyum, Abdul Raden Arief Nugroho Raden Roro Sisilia Sulistiyaningsih Rahmawati, Ade Irma Rahmawati, Alfiani Diah Rasilah, Rasilah Riski Yunanto Saidah, Nur Saifudin Zuhri Setiawardani, Wawan Sindhu Rakasiwi Siska Dewi Siswanto, Edy Sukemi Kamto Sudibyo Syairaji, M. Syamsul Hadi Syamsul Hadi Syarifah Salma Titik Rianawati Valentina Widya Suryaningtyas, Valentina Widya Vetriana Agustiningrum Vintya Meiliana Putri Nugraheni Vivi Kumalasari Vivi Kumalasari, Vivi Wahyono Wenti Ayu Sunarjo Wibowo, Moh Edi Wildan, Muh Khaerul Wiwid Wahyudi