Subword-Level Language Identification for Intra-Word Code-Switching
This addresses a domain-specific problem for researchers and practitioners in multilingual NLP, focusing on morphologically rich languages, and is incremental as it builds on existing token-level approaches.
The paper tackles the problem of language identification for intra-word code-switching by extending it to the subword-level to handle mixed words, proposing a segmental recurrent neural network model. The model performs slightly better or on par with baselines overall but strongly outperforms them specifically on mixed words.
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.