Neural Automated Writing Evaluation with Corrective Feedback
Izia Xiaoxiao Wang Xihan Wu Edith Coates Min Zeng Jiexin Kuang Siliang Liu Mengyang Qiu Jungyeul Park
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.