Investigating neural architectures for short answer scoring

Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch, Chong Min Lee


Abstract
Neural approaches to automated essay scoring have recently shown state-of-the-art performance. The automated essay scoring task typically involves a broad notion of writing quality that encompasses content, grammar, organization, and conventions. This differs from the short answer content scoring task, which focuses on content accuracy. The inputs to neural essay scoring models – ngrams and embeddings – are arguably well-suited to evaluate content in short answer scoring tasks. We investigate how several basic neural approaches similar to those used for automated essay scoring perform on short answer scoring. We show 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 short answer scoring.
Anthology ID:
W17-5017
Volume:
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
159–168
Language:
URL:
https://aclanthology.org/W17-5017
DOI:
10.18653/v1/W17-5017
Bibkey:
Cite (ACL):
Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch, and Chong Min Lee. 2017. Investigating neural architectures for short answer scoring. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 159–168, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Investigating neural architectures for short answer scoring (Riordan et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5017.pdf