Abstract
Automated methods for essay scoring have made great progress in recent years, achieving accuracies very close to human annotators. However, a known weakness of such automated scorers is not taking into account the semantic relevance of the submitted text. While there is existing work on detecting answer relevance given a textual prompt, very little previous research has been done to incorporate visual writing prompts. We propose a neural architecture and several extensions for detecting off-topic responses to visual prompts and evaluate it on a dataset of texts written by language learners.- Anthology ID:
- W17-5020
- 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:
- 188–197
- Language:
- URL:
- https://aclanthology.org/W17-5020
- DOI:
- 10.18653/v1/W17-5020
- Cite (ACL):
- Marek Rei. 2017. Detecting Off-topic Responses to Visual Prompts. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 188–197, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Off-topic Responses to Visual Prompts (Rei, BEA 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W17-5020.pdf
- Data
- Flickr30k