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
- Editors:
- Joel Tetreault, Jill Burstein, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 159–168
- Language:
- URL:
- https://aclanthology.org/W17-5017
- DOI:
- 10.18653/v1/W17-5017
- 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., BEA 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W17-5017.pdf