The paper presents an integrated system that combines automated writing evaluation (AWE) and grammatical error correction (GEC) to enhance the language learning process for second language (L2) learners.
The system enables language learners to simulate examination situations, where they can submit their written essays, and the system will provide them with both assessment of the writing quality and corrective feedback on grammatical errors. This integrated approach aims to bridge the gap between AWE and GEC, offering a more comprehensive solution compared to standalone systems.
The AWE component utilizes neural models to provide holistic scores and rubric-based evaluations, drawing on the ASAP and ASAP++ datasets. The GEC component employs sequence-to-sequence and edit-based neural models, leveraging various pre-trained language models such as BERT and BART, to detect and correct grammatical errors.
The authors discuss the potential of the system to facilitate language education, alleviate the burden on instructors, and empower learners to "write it right" through simulated examination scenarios. They also outline plans to expand the system to support multilingual inputs and incorporate instructor feedback into the workflow.
The paper highlights the importance of integrating AWE and GEC to provide comprehensive and efficient feedback, which can significantly improve the language learning experience for L2 learners.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Izia Xiaoxia... at arxiv.org 05-07-2024
https://arxiv.org/pdf/2402.17613.pdfDeeper Inquiries