The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Automated Essay Scoring with Discourse-Aware Neural Models
Open accessAbstract
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.
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Discourse markers can be used to measure the coherence quality of a text.
“Discourse markers can be used to measure the coherence quality of a text.”
From A large-scale comparison of human-written versus ChatGPT-generated essays (2023) · cites this paper as [54]
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.11 · Cited by 2,264
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
Peters, M. E., Ruder, S., & Smith, N. A. (2019) · Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) · doi:10.18653/v1/w19-4302 · Cited by 432
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A large-scale comparison of human-written versus ChatGPT-generated essays
Herbold, S., Hautli-Janisz, A., Heuer, U., Kikteva, Z., & Trautsch, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-45644-9 · Cited by 338
“This has been explored by Somasundaran et al. 53 who use discourse markers to evaluate the story-telling aspect of student writing while Nadeem et al. 54 incorporated them in their deep learning-based approach to automated essay scoring.”
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139
Metadata
- Authors
- Nadeem, F., Nguyen, H., Liu, Y., & Ostendorf, M.
- Affiliations
- University of Washington (US)
- ORCID
- Liu, Yang
- Venue
- Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Published
- 2019
- Pages
- 484-493
- OpenAlex
- W2970811405
- Cited by
- 81
- References
- 5
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 12 · 2023: 16 · 2022: 12 · 2021: 14 · 2020: 12
- FWCI
- 3.63
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Innovative Use of NLP for Building Educational Applications (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Keywords
- automated essay scoring, contextual embeddings, pre-training strategies, neural network architecture, feature engineering, low-resource training data
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