@inproceedings{septiandri-etal-2020-knowing,
    title = "Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in {B}ahasa {I}ndonesia?",
    author = "Septiandri, Ali Akbar  and
      Winatmoko, Yosef Ardhito  and
      Putra, Ilham Firdausi",
    editor = "Moosavi, Nafise Sadat  and
      Fan, Angela  and
      Shwartz, Vered  and
      Glava{\v{s}}, Goran  and
      Joty, Shafiq  and
      Wang, Alex  and
      Wolf, Thomas",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.sustainlp-1.1",
    doi = "10.18653/v1/2020.sustainlp-1.1",
    pages = "1--7",
    abstract = "We compare three solutions to UKARA 1.0 challenge on automated short-answer scoring: single classical, ensemble classical, and deep learning. The task is to classify given answers to two questions, whether they are right or wrong. While recent development shows increasing model complexity to push the benchmark performances, they tend to be resource-demanding with mundane improvement. For the UKARA task, we found that bag-of-words and classical machine learning approaches can compete with ensemble models and Bi-LSTM model with pre-trained word2vec embedding from 200 million words. In this case, the single classical machine learning achieved less than 2{\%} difference in F1 compared to the deep learning approach with 1/18 time for model training.",
}