On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation

Yongjie Wang, Chuang Wang, Ruobing Li, Hui Lin


Abstract
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.
Anthology ID:
2022.naacl-main.249
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3416–3425
Language:
URL:
https://aclanthology.org/2022.naacl-main.249
DOI:
10.18653/v1/2022.naacl-main.249
Bibkey:
Cite (ACL):
Yongjie Wang, Chuang Wang, Ruobing Li, and Hui Lin. 2022. On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3416–3425, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation (Wang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.249.pdf
Code
 lingochamp/multi-scale-bert-aes
Data
ASAP