Abstract
While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.- Anthology ID:
- 2020.acl-main.759
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8569–8584
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.759
- DOI:
- 10.18653/v1/2020.acl-main.759
- Cite (ACL):
- Haoran Zhang and Diane Litman. 2020. Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8569–8584, Online. Association for Computational Linguistics.
- Cite (Informal):
- Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring (Zhang & Litman, ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.759.pdf