Improving Language Identification for Multilingual Speakers

Many language-related tasks, such as entering text on your iPhone, discovering news articles you might enjoy, or finding out answers to questions you may have, are powered by language-specific natural language processing (NLP) models. To decide which model to invoke at a particular point in time, we must perform language identification (LID), often on the basis of limited evidence, namely a short character string. Performing reliable LID is more critical than ever as multi-lingual input is becoming more and more common across all Apple platforms. In most writing scripts — like Latin and Cyrillic, but also including Hanzi, Arabic, and others — strings composed of a few characters are often present in more than one language, making reliable identification challenging. In this article, we explore how we can improve LID accuracy by treating it as a sequence labeling problem at the character level, and using bi-directional long short-term memory (bi-LSTM) neural networks trained on short character sequences. We observed reductions in error rates varying from 15% to 60%, depending on the language, while achieving reductions in model size between 40% and 80% compared to previously shipping solutions. Thus the LSTM LID approach helped us identify language more correctly in features such as QuickType keyboards and Smart Responses, thereby leading to better auto-corrections, completions, and predictions, and ultimately a more satisfying user experience. It also made public APIs like the Natural Language framework more robust to multi-lingual environments.