Subword-Level Language Identification for Intra-Word Code-Switching

ACL 2019

Subword-Level Language Identification for Intra-Word Code-Switching

Jan 19, 2021
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Abstract: 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. Authors: Manuel Mager, Özlem Çetinoğlu, Katharina Kann (University of Stuttgart, New York University)

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