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.

本文将语种识别任务推广到子词级别，研究如何识别包含两种以上语言的语素（intra-word CS），提出了一种基于分段循环神经网络的模型，并在西班牙 - 韦萨里卡数据集和德国 - 土耳其数据集的实验中，相较于基准方法有略微提升或相当的表现。