TL;DR
This paper introduces a neural approach to identify off-topic responses to visual prompts, addressing a gap in automated essay scoring by considering visual context, and evaluates it on learner-written texts.
Contribution
It presents a novel neural architecture specifically designed for detecting relevance in responses to visual prompts, extending prior work focused mainly on textual prompts.
Findings
Effective detection of off-topic responses to visual prompts.
Improved relevance classification accuracy on learner data.
Extensions enhance the model's performance and robustness.
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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