Paper Info
Title
(Almost) Unsupervised Grammatical Error Correction Using A Synthetic Comparable Corpus
Abstract
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.
Year
DOI
Venue
2019
10.18653/v1/w19-4413
INNOVATIVE USE OF NLP FOR BUILDING EDUCATIONAL APPLICATIONS
Field
DocType
Citations 
Computer science,Error detection and correction,Speech recognition
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Satoru Katsumata103.38
Mamoru Komachi224144.56