Native Language Identification, or NLI, is the task of automatically classifying the L1 of a writer based solely on his or her essay written in another language. This problem area has seen a spike in interest in recent years as it can have an impact on educational applications tailored towards non-native speakers of a language, as well as authorship profiling. While there has been a growing body of work in NLI, it has been difficult to compare methodologies because of the different approaches to pre-processing the data, different sets of languages identified, and different splits of the data used. In this shared task, the first ever for Native Language Identification, we sought to address the above issues by providing a large corpus designed specifically for NLI, in addition to providing an environment for systems to be directly compared. In this paper, we report the results of the shared task. A total of 29 teams from around the world competed across three different sub-tasks.
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