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2021

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A reproduction of Apple’s bi-directional LSTM models for language identification in short strings
Mads Toftrup | Søren Asger Sørensen | Manuel R. Ciosici | Ira Assent
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

Language Identification is the task of identifying a document’s language. For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments. In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog post. We confirm the bi-LSTM model’s performance and find that it outperforms current open-source language identifiers. We further find that its language identification mistakes are due to confusion between related languages.
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