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Automatic Question Generation using RNN-based and Pre-trained Transformer-based Models in Low Resource Indonesian Language
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DOI：10.31449/inf.v46i7.4236.png)
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Abstract
En 中文
Although Indonesian is the fourth most frequently used language on the internet, the development of NLP in Indonesian has not been studied intensively. One form of NLP application classified as an NLG task is the Automatic Question Generation task. Generally, the task has proven well, us-ing rule-based and cloze tests, but these approaches depend heavily on the defined rules. While this approach is suitable for automated question generation systems on a small scale, it can be-come less efficient as the scale of the system grows. Many NLG model architectures have recently proven to have significantly improved performance compared to previous architectures, such as generative pre-trained transformers, text-to-text transfer transformers, bidirectional auto -regressive transformers, and many more. Previous studies on AQG in Indonesian were built on RNN-based architecture such as GRU, LSTM, and Transformer. The performance of models in previous studies is compared with state-of-the-art models, such as multilingual models mBART and mT5, and monolingual models such as IndoBART and IndoGPT. As a result, the fine-tuned IndoBART performed significantly higher than either BiGRU and BiLSTM on the SQuAD dataset. Fine-tuned IndoBART on most of the metrics also performed better on the TyDiQA dataset only, which has fewer population than the SQuAD dataset.
Keywords:
natural language processing
natural language generation
automatic question generation
recurrent neural network
long-short term memory
gated recurrent unit
transformer
fine-tuning
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