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Evaluation and annotation are two of the greatest challenges in developing NLP instructional or diagnostic tools to mark grammar and usage errors in the writing of non-native speakers. Past approaches have commonly used only one rater to annotate a corpus of learner errors to compare to system output. In this paper, we show how using only one rater can skew system evaluation and then we present a sampling approach that makes it possible to evaluate a system more efficiently.

Native judgments of non-native usage