Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in Bahasa Indonesia?

AbstractWe 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.