  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.
