UKARA 1.0 Challenge Track 1: Automatic Short-Answer Scoring in Bahasa Indonesia
This work addresses automated essay scoring for educational applications in Bahasa Indonesia, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of automatic short-answer scoring in Bahasa Indonesia by developing a third-place solution for the UKARA 1.0 challenge, achieving an F1 score of 0.812 using different models for two binary classification tasks.
We describe our third-place solution to the UKARA 1.0 challenge on automated essay scoring. The task consists of a binary classification problem on two datasets | answers from two different questions. We ended up using two different models for the two datasets. For task A, we applied a random forest algorithm on features extracted using unigram with latent semantic analysis (LSA). On the other hand, for task B, we only used logistic regression on TF-IDF features. Our model results in F1 score of 0.812.