  We address fine-grained multilingual language identification: providing a
language code for every token in a sentence, including codemixed text
containing multiple languages. Such text is prevalent online, in documents,
social media, and message boards. We show that a feed-forward network with a
simple globally constrained decoder can accurately and rapidly label both
codemixed and monolingual text in 100 languages and 100 language pairs. This
model outperforms previously published multilingual approaches in terms of both
accuracy and speed, yielding an 800x speed-up and a 19.5% averaged absolute
gain on three codemixed datasets. It furthermore outperforms several benchmark
systems on monolingual language identification.
