TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring
Abstract
Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for non-target prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.- Anthology ID:
- P18-1100
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1088–1097
- Language:
- URL:
- https://aclanthology.org/P18-1100
- DOI:
- 10.18653/v1/P18-1100
- Cite (ACL):
- Cancan Jin, Ben He, Kai Hui, and Le Sun. 2018. TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1088–1097, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring (Jin et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P18-1100.pdf