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This article describes an unsupervised language model adaptation approachthat can be used to enhance the performance of language identification methods.The approach is applied to a current version of the HeLI languageidentification method, which is now called HeLI 2.0. We describe the HeLI 2.0method in detail. The resulting system is evaluated using the datasets from theGerman dialect identification and Indo-Aryan language identification sharedtasks of the VarDial workshops 2017 and 2018. The new approach with languageidentification provides considerably higher F1-scores than the previous HeLImethod or the other systems which participated in the shared tasks. The resultsindicate that unsupervised language model adaptation should be considered as anoption in all language identification tasks, especially in those whereencountering out-of-domain data is likely.

Language Model Adaptation for Language and Dialect Identification of Text