Grammatical error correction can be viewed as a low-resource sequence-to-sequence task,
because publicly available parallel corpora are limited. To tackle this challenge, we first …

Written answers to open-ended questions can have a higher long-term effect on learning
than multiple-choice questions. However, it is critical that teachers immediately review the …

We focus on two leading state-of-the-art approaches to grammatical error correction–
machine learning classification and machine translation. Based on the comparative study of …

We build a grammatical error correction (GEC) system primarily based on the state-of-the-art
statistical machine translation (SMT) approach, using task-specific features and tuning, and …

Automatic grammatical error correction (GEC) research has made remarkable progress in
the past decade. However, all existing approaches to GEC correct errors by considering a …

Notable mean score differences for the e‐rater® automated scoring engine and for humans
for essays from certain demographic groups were observed for the GRE® General Test in …

Spelling correction has attracted a lot of attention in the NLP community. However, models
have been usually evaluated on artificiallycreated or proprietary corpora. A publiclyavailable …

Automated computerized scoring systems (ACSSs) are being increasingly used to analyze
text in many educational settings. Nevertheless, the impact of misspelled words (MSW) on …

Abstract Grammatical Error Correction (GEC) is concerned with correcting grammatical
errors in written text. Current GEC systems, namely those leveraging statistical and neural …

Automatic spelling correction stands as a pivotal challenge within the ambit of natural
language processing (NLP), demanding nuanced solutions. Traditional spelling correction …