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Grammatical error detection (GED) in non-native writing requires systems toidentify a wide range of errors in text written by language learners. Errordetection as a purely supervised task can be challenging, as GED datasets arelimited in size and the label distributions are highly imbalanced.Contextualized word representations offer a possible solution, as they canefficiently capture compositional information in language and can be optimizedon large amounts of unsupervised data. In this paper, we perform a systematiccomparison of ELMo, BERT and Flair embeddings (Peters et al., 2017; Devlin etal., 2018; Akbik et al., 2018) on a range of public GED datasets, and proposean approach to effectively integrate such representations in current methods,achieving a new state of the art on GED. We further analyze the strengths andweaknesses of different contextual embeddings for the task at hand, and presentdetailed analyses of their impact on different types of errors.

Context is Key: Grammatical Error Detection with Contextual Word Representations