  Language identification for code-switching (CS), the phenomenon of
alternating between two or more languages in conversations, has traditionally
been approached under the assumption of a single language per token. However,
if at least one language is morphologically rich, a large number of words can
be composed of morphemes from more than one language (intra-word CS). In this
paper, we extend the language identification task to the subword-level, such
that it includes splitting mixed words while tagging each part with a language
ID. We further propose a model for this task, which is based on a segmental
recurrent neural network. In experiments on a new Spanish--Wixarika dataset and
on an adapted German--Turkish dataset, our proposed model performs slightly
better than or roughly on par with our best baseline, respectively. Considering
only mixed words, however, it strongly outperforms all baselines.
