Neural Automated Writing Evaluation with Corrective Feedback

TL;DR

This paper introduces an integrated NLP-based system that combines automated writing evaluation and grammatical error correction to provide instant feedback and scoring for second language learners, improving efficiency and learning outcomes.

Contribution

The paper presents a novel system that unifies automated scoring and grammatical correction, bridging the gap between AWE and GEC for enhanced language learning support.

Findings

System effectively assesses writing and provides corrections

Reduces manual grading effort and time

Enhances feedback accuracy and learner engagement

Abstract

The utilization of technology in second language learning and teaching has become ubiquitous. For the assessment of writing specifically, automated writing evaluation (AWE) and grammatical error correction (GEC) have become immensely popular and effective methods for enhancing writing proficiency and delivering instant and individualized feedback to learners. By leveraging the power of natural language processing (NLP) and machine learning algorithms, AWE and GEC systems have been developed separately to provide language learners with automated corrective feedback and more accurate and unbiased scoring that would otherwise be subject to examiners. In this paper, we propose an integrated system for automated writing evaluation with corrective feedback as a means of bridging the gap between AWE and GEC results for second language learners. This system enables language learners to simulate the essay writing tests: a student writes and submits an essay, and the system returns the assessment of the writing along with suggested grammatical error corrections. Given that automated scoring and grammatical correction are more efficient and cost-effective than human grading, this integrated system would also alleviate the burden of manually correcting innumerable essays.