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Lexical Normalization for Code-switched Data and its Effect on POS Tagging

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Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of many natural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media. In this paper, we propose three normalization models specifically designed to handle code-switched data which we evaluate for two language pairs: Indonesian-English and Turkish-German. For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging. Results show that our CS-tailored normalization models significantly outperform monolingual ones, and lead to 5.4\% relative performance increase for POS tagging as compared to unnormalized input.
Original languageEnglish
Title of host publicationProceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Number of pages13
PublisherAssociation for Computational Linguistics
Publication dateApr 2021
Pages2352-2365
Publication statusPublished - Apr 2021
EventEACL 2021 -
Duration: 19 Apr 202123 Apr 2021

Conference

ConferenceEACL 2021
Periode19/04/202123/04/2021

ID: 85929484