Automated essay scoring is one of the most important problem in Natural Language Processing. It has been explored for a number of years, and it remains partially solved. In addition to its economic and educational usefulness, it presents research problems. Transfer learning has proved to be beneficial 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.
翻译:自动作文评分是自然语言处理中最重要的问题之一,已经探讨了若干年,并且仍然部分地得到解决。除了其经济和教育用途外,它还提出了研究问题。转移学习已证明对NLP有益。数据增强技术还帮助建立了最先进的自动作文评分模型。过去许多工作试图通过使用RNS、LSTMs等方法解决这个问题。这项工作审查了变压器模型,如BERT、RoBERTA等。我们从经验上证明了变压器模型和数据扩增的有效性,以便用单一模型对许多专题进行自动作文评。