A reproduction of Apple's bi-directional LSTM models for language identification in short strings

Abstract

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.

Keywords

• Computational linguistics
• Long short-term memory
• Language identification
• Open source language
• NLP