Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stacking Neural Network Models for Automatic Short Answer Scoring   Automatic short answer scoring is one of the text classification problems to
assess students' answers during exams automatically. Several challenges can
arise in making an automatic short answer scoring system, one of which is the
quantity and quality of the data. The data labeling process is not easy because
it requires a human annotator who is an expert in their field. Further, the
data imbalance process is also a challenge because the number of labels for
correct answers is always much less than the wrong answers. In this paper, we
propose the use of a stacking model based on neural network and XGBoost for
classification process with sentence embedding feature. We also propose to use
data upsampling method to handle imbalance classes and hyperparameters
optimization algorithm to find a robust model automatically. We use Ukara 1.0
Challenge dataset and our best model obtained an F1-score of 0.821 exceeding
the previous work at the same dataset.

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The categories of this paper are: econ
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat