TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring

Cancan Jin, Ben He, Kai Hui, Le Sun


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P18-1100.pdf
Presentation:
 P18-1100.Presentation.pdf
Video:
 https://vimeo.com/285802257