论文标题
使用变压器模型的自动论文评分的数据增强
Data Augmentation for Automated Essay Scoring using Transformer Models
论文作者
论文摘要
自动论文评分是自然语言处理中最重要的问题之一。它已经探索了多年，并且仍在部分解决。除了其经济和教育实用性外，它还提出了研究问题。事实证明，转移学习在NLP中是有益的。数据增强技术还帮助建立了自动论文评分的最新模型。过去，许多工作都试图通过使用RNN，LSTMS等来解决此问题。这项工作研究了Transformer模型，例如Bert，Roberta等。我们经验证明了Transformer模型和数据增强的有效性，并使用单个模型进行了许多主题的自动化论文分级。
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.
