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Data Strategies for Low-Resource Grammatical Error Correction
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.
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses
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.
Noisy Channel for Low Resource Grammatical Error Correction
This paper describes our contribution to the low-resource track of the BEA 2019 shared task on Grammatical Error Correction (GEC).
Historical Text Normalization with Delayed Rewards
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.
A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors
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.
