Hierarchical Multi-Task Learning for Organization Evaluation of Argumentative Student Essays
Abstract
Organization evaluation is an important dimension of automated essay scoring. This paper focuses on discourse element (i.e., functions of sentences and paragraphs) based organization evaluation. Existing approaches mostly separate discourse element identification and organization evaluation. In contrast, we propose a neural hierarchical multi-task learning approach for jointly optimizing sentence and paragraph level discourse element identification and organization evaluation. We represent the organization as a grid to simulate the visual layout of an essay and integrate discourse elements at multiple linguistic levels. Experimental results show that the multi-task learning based organization evaluation can achieve significant improvements compared with existing work and pipeline baselines. Multiple level discourse element identification also benefits from multi-task learning through mutual enhancement.
Cite
Text
Song et al. "Hierarchical Multi-Task Learning for Organization Evaluation of Argumentative Student Essays." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/536Markdown
[Song et al. "Hierarchical Multi-Task Learning for Organization Evaluation of Argumentative Student Essays." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/song2020ijcai-hierarchical/) doi:10.24963/IJCAI.2020/536BibTeX
@inproceedings{song2020ijcai-hierarchical,
title = {{Hierarchical Multi-Task Learning for Organization Evaluation of Argumentative Student Essays}},
author = {Song, Wei and Song, Ziyao and Liu, Lizhen and Fu, Ruiji},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {3875-3881},
doi = {10.24963/IJCAI.2020/536},
url = {https://mlanthology.org/ijcai/2020/song2020ijcai-hierarchical/}
}