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• W2953083125 abstract "Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners. Error detection as a purely supervised task can be challenging, as GED datasets are limited in size and the label distributions are highly imbalanced. Contextualized word representations offer a possible solution, as they can efficiently capture compositional information in language and can be optimized on large amounts of unsupervised data. In this paper, we perform a systematic comparison of ELMo, BERT and Flair embeddings (Peters et al., 2017; Devlin et al., 2018; Akbik et al., 2018) on a range of public GED datasets, and propose an approach to effectively integrate such representations in current methods, achieving a new state of the art on GED. We further analyze the strengths and weaknesses of different contextual embeddings for the task at hand, and present detailed analyses of their impact on different types of errors." @default.
• W2953083125 created "2019-06-27" @default.
• W2953083125 creator A5062209641 @default.
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• W2953083125 date "2019-01-01" @default.
• W2953083125 modified "2023-10-01" @default.
• W2953083125 title "Context is Key: Grammatical Error Detection with Contextual Word Representations" @default.
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• W2953083125 doi "https://doi.org/10.18653/v1/w19-4410" @default.
• W2953083125 hasPublicationYear "2019" @default.
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