Detecting Off-topic Responses to Visual Prompts

Marek Rei


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/W17-5020.pdf
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