Unsupervised modeling of topical relevance in l2 learner text

Abstract

The automated scoring of second-language (L2) learner text along various writing dimensions is an increasingly active research area. In this paper, we focus on determining the topical relevance of an essay to the prompt that elicited it. Given the burden involved in manually assigning scores for use in training supervised prompt-relevance models, we develop unsupervised models and show that they correlate well with human judgements. We show that expanding prompts using topically-related words, via pseudo-relevance modelling, is beneficial and outperforms other distributional techniques. Finally, we incorporate our prompt-relevance models into a supervised essay scoring system that predicts a holistic score and show that it improves its performance.