Authors:
Keith Cochran
1
;
2
;
Clayton Cohn
1
;
2
and
Peter Hastings
1
;
2
Affiliations:
1
DePaul University, Chicago IL 60604, U.S.A.
;
2
Vanderbilt University, Nashville TN 37240, U.S.A.
Keyword(s):
Educational Texts, Natural Language Processing, BERT, Data Augmentation, Text Augmentation, Imbalanced Data Sets.
Abstract:
Computer-supported education studies can perform two important roles. They can allow researchers to gather important data about student learning processes, and they can help students learn more efficiently and effectively by providing automatic immediate feedback on what the students have done so far. The evaluation of student work required for both of these roles can be relatively easy in domains like math, where there are clear right answers. When text is involved, however, automated evaluations become more difficult. Natural Language Processing (NLP) can provide quick evaluations of student texts. However, traditional neural network approaches require a large amount of data to train models with enough accuracy to be useful in analyzing student responses. Typically, educational studies collect data but often only in small amounts and with a narrow focus on a particular topic. BERT-based neural network models have revolutionized NLP because they are pre-trained on very large corpora
, developing a robust, contextualized understanding of the language. Then they can be “fine-tuned” on a much smaller set of data for a particular task. However, these models still need a certain base level of training data to be reasonably accurate, and that base level can exceed that provided by educational applications, which might contain only a few dozen examples. In other areas of artificial intelligence, such as computer vision, model performance on small data sets has been improved by “data augmentation” — adding scaled and rotated versions of the original images to the training set. This has been attempted on textual data; however, augmenting text is much more difficult than simply scaling or rotating images. The newly generated sentences may not be semantically similar to the original sentence, resulting in an improperly trained model. In this paper, we examine a self-augmentation method that is straightforward and shows great improvements in performance with different BERT-based models in two different languages and on two different tasks which have small data sets. We also identify the limitations of the self-augmentation procedure.
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