Investigating neural architectures for short answer scoring
Article Status
Published
Authors/contributors
- Riordan, Brian (Author)
- Horbach, Andrea (Author)
- Cahill, Aoife (Author)
- Zesch, Torsten (Author)
- Lee, Chong Min (Author)
Title
Investigating neural architectures for short answer scoring
Date
2017
Proceedings Title
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Conference Name
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Place
Copenhagen, Denmark
Publisher
Association for Computational Linguistics
Pages
159-168
Language
en
Accessed
22/02/2023, 22:14
Library Catalogue
DOI.org (Crossref)
Extra
Citation Key: riordan2017
<标题>: 研究用于简答评分的神经架构
<AI Smry>: This work investigates how several basic neural approaches similar to those used for automated essay scoring perform on short answer scoring, and shows that neural architectures can outperform a strong non-neural baseline, but performance and optimal parameter settings vary across the more diverse types of prompts typical of shortanswer scoring.
Citation
Riordan, B., Horbach, A., Cahill, A., Zesch, T., & Lee, C. M. (2017). Investigating neural architectures for short answer scoring. Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, 159–168. https://doi.org/10.18653/v1/W17-5017
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