Detecting Off-topic Responses to Visual Prompts
Computation and Language
2017-07-18 v1 Machine Learning
Neural and Evolutionary Computing
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.
Cite
@article{arxiv.1707.05233,
title = {Detecting Off-topic Responses to Visual Prompts},
author = {Marek Rei},
journal= {arXiv preprint arXiv:1707.05233},
year = {2017}
}
Comments
The 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2017)