  In recent years, pre-trained models have become dominant in most natural
language processing (NLP) tasks. However, in the area of Automated Essay
Scoring (AES), pre-trained models such as BERT have not been properly used to
outperform other deep learning models such as LSTM. In this paper, we introduce
a novel multi-scale essay representation for BERT that can be jointly learned.
We also employ multiple losses and transfer learning from out-of-domain essays
to further improve the performance. Experiment results show that our approach
derives much benefit from joint learning of multi-scale essay representation
and obtains almost the state-of-the-art result among all deep learning models
in the ASAP task. Our multi-scale essay representation also generalizes well to
CommonLit Readability Prize data set, which suggests that the novel text
representation proposed in this paper may be a new and effective choice for
long-text tasks.
