Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus
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 various GEC dataset, includi ng a low resource track of the shared task at Building Educational Applications 2019 (BEA 2019). As a result, we achieved an F_0.5 score of 28.31 points with the test data of the low resource track.
Code (0)
등록된 구현이 없습니다.
Tasks
Grammatical Error CorrectionMachine TranslationTranslationSimilar Papers 제목 키워드 기반
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
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 syst…
Grammatical Error CorrectionMachine TranslationTranslationA Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction
Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text. Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of ann…
Grammatical Error CorrectionMachine TranslationSynthetic Data GenerationTranslation