(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
Satoru Katsumata, Mamoru Komachi · 2019
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation.We verified our GEC system through experiments on a low resource track of the shared task at Building Educational Applications 2019 (BEA2019).As a result, we achieved an F 0.5 score of 28.31 points with the test data.