Incorporating Dialectal Variability for Socially Equitable Language Identification

Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-tosequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-theart performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of "socially inclusive" NLP tools.