@inproceedings{rei-2017-detecting,
title = "Detecting Off-topic Responses to Visual Prompts",
author = "Rei, Marek",
editor = "Tetreault, Joel and
Burstein, Jill and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5020",
doi = "10.18653/v1/W17-5020",
pages = "188--197",
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.",
}
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%0 Conference Proceedings
%T Detecting Off-topic Responses to Visual Prompts
%A Rei, Marek
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F rei-2017-detecting
%X 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.
%R 10.18653/v1/W17-5020
%U https://aclanthology.org/W17-5020
%U https://doi.org/10.18653/v1/W17-5020
%P 188-197
Markdown (Informal)
[Detecting Off-topic Responses to Visual Prompts](https://aclanthology.org/W17-5020) (Rei, BEA 2017)
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.