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To date, efforts in the code-switching literature have focused for the mostpart on language identification, POS, NER, and syntactic parsing. In thispaper, we address machine translation for code-switched social media data. Wecreate a community shared task. We provide two modalities for participation:supervised and unsupervised. For the supervised setting, participants arechallenged to translate English into Hindi-English (Eng-Hinglish) in a singledirection. For the unsupervised setting, we provide the following languagepairs: English and Spanish-English (Eng-Spanglish), and English and ModernStandard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions. We shareinsights and challenges in curating the "into" code-switching languageevaluation data. Further, we provide baselines for all language pairs in theshared task. The leaderboard for the shared task comprises 12 individual systemsubmissions corresponding to 5 different teams. The best performance achievedis 12.67% BLEU score for English to Hinglish and 25.72% BLEU score for MSAEA toEnglish.

CALCS 2021 Shared Task: Machine Translation for Code-Switched Data