Investigating neural architectures for short answer scoring

Abstract

Neural approaches to automated essay scoring have recently shown state-of-theart performance. The automated essay scoring task typically involves a broad notion of writing quality that encompasses content, grammar, organization, and conventions. This differs from the short answer content scoring task, which focuses on content accuracy. The inputs to neural essay scoring models - ngrams and embeddings - are arguably well-suited to evaluate content in short answer scoring tasks. We investigate how several basic neural approaches similar to those used for automated essay scoring perform on short answer scoring. We show that neural architectures can outperform a strong nonneural baseline, but performance and optimal parameter settings vary across the more diverse types of prompts typical of short answer scoring.