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Being able to parse code-switched (CS) utterances, such as Spanish+English orHindi+English, is essential to democratize task-oriented semantic parsingsystems for certain locales. In this work, we focus on Spanglish(Spanish+English) and release a dataset, CSTOP, containing 5800 CS utterancesalongside their semantic parses. We examine the CS generalizability of variousCross-lingual (XL) models and exhibit the advantage of pre-trained XL languagemodels when data for only one language is present. As such, we focus onimproving the pre-trained models for the case when only English corpusalongside either zero or a few CS training instances are available. We proposetwo data augmentation methods for the zero-shot and the few-shot settings:fine-tune using translate-and-align and augment using a generation modelfollowed by match-and-filter. Combining the few-shot setting with the aboveimprovements decreases the initial 30-point accuracy gap between the zero-shotand the full-data settings by two thirds.

El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing