  In language identification, a common first step in natural language
processing, we want to automatically determine the language of some input text.
Monolingual language identification assumes that the given document is written
in one language. In multilingual language identification, the document is
usually in two or three languages and we just want their names. We aim one step
further and propose a method for textual language identification where
languages can change arbitrarily and the goal is to identify the spans of each
of the languages. Our method is based on Bidirectional Recurrent Neural
Networks and it performs well in monolingual and multilingual language
identification tasks on six datasets covering 131 languages. The method keeps
the accuracy also for short documents and across domains, so it is ideal for
off-the-shelf use without preparation of training data.
