no code implementations • EACL (BEA) 2021 • Simon Flachs, Felix Stahlberg, Shankar Kumar
We investigate how best to take advantage of existing data sources for improving GEC systems for languages with limited quantities of high quality training data.
no code implementations • EMNLP 2020 • Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis, Marek Rei, Anders Søgaard
Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications.
no code implementations • WS 2019 • Simon Flachs, Oph{\'e}lie Lacroix, Anders S{\o}gaard
This paper describes our contribution to the low-resource track of the BEA 2019 shared task on Grammatical Error Correction (GEC).
no code implementations • ACL 2019 • Simon Flachs, Marcel Bollmann, Anders S{\o}gaard
Training neural sequence-to-sequence models with simple token-level log-likelihood is now a standard approach to historical text normalization, albeit often outperformed by phrase-based models.
no code implementations • NAACL 2019 • Simon Flachs, Oph{\'e}lie Lacroix, Marek Rei, Helen Yannakoudakis, Anders S{\o}gaard
While rule-based detection of subject-verb agreement (SVA) errors is sensitive to syntactic parsing errors and irregularities and exceptions to the main rules, neural sequential labelers have a tendency to overfit their training data.