Automated Essay Scoring using Transformers
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Despite being investigated for over five decades, the task of automated essay scoring continues to draw a lot of attention in the NLP community, in part because of its commercial and educational values as well as the associated research challenges. Large pre-trained models have made remarkable progress in NLP. Data augmentation techniques have also helped build state-of-the-art models for automated essay scoring. Many works in the past have attempted to solve this problem by using RNNs, LSTMs, etc. This work examines the transformer models like BERT, RoBERTa, etc. We empirically demonstrate the effectiveness of transformer models and data augmentation for automated essay grading across many topics using a single model.
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