Automated Essay Scoring with Discourse-Aware Neural Models

Farah Nadeem, Huy Nguyen, Yang Liu, Mari Ostendorf


Abstract
Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering. Neural networks offer an alternative to feature engineering, but they typically require more annotated data. This paper explores network structures, contextualized embeddings and pre-training strategies aimed at capturing discourse characteristics of essays. Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available.
Anthology ID:
W19-4450
Volume:
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
484–493
Language:
URL:
https://aclanthology.org/W19-4450
DOI:
10.18653/v1/W19-4450
Bibkey:
Cite (ACL):
Farah Nadeem, Huy Nguyen, Yang Liu, and Mari Ostendorf. 2019. Automated Essay Scoring with Discourse-Aware Neural Models. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 484–493, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Automated Essay Scoring with Discourse-Aware Neural Models (Nadeem et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-4450.pdf
Data
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