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UKARA 1.0 Challenge Track 1: Automatic Short-Answer Scoring in Bahasa Indonesia
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UKARA 1.0 Challenge Track 1: Automatic Short-Answer Scoring in Bahasa Indonesia
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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.
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