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

• URL: http://arxiv.org/abs/2202.09625v1
• Date: Sat, 19 Feb 2022 15:39:34 GMT
• Title: CALCS 2021 Shared Task: Machine Translation for Code-Switched Data
• Authors: Shuguang Chen, Gustavo Aguilar, Anirudh Srinivasan, Mona Diab and Thamar Solorio
• Abstract summary: We address machine translation for code-switched social media data. We create a community shared task. For the supervised setting, participants are challenged to translate English into Hindi-English (Eng-Hinglish) in a single direction. For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions.
• Score: 27.28423961505655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To date, efforts in the code-switching literature have focused for the most part on language identification, POS, NER, and syntactic parsing. In this paper, we address machine translation for code-switched social media data. We create a community shared task. We provide two modalities for participation: supervised and unsupervised. For the supervised setting, participants are challenged to translate English into Hindi-English (Eng-Hinglish) in a single direction. For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions. We share insights and challenges in curating the "into" code-switching language evaluation data. Further, we provide baselines for all language pairs in the shared task. The leaderboard for the shared task comprises 12 individual system submissions corresponding to 5 different teams. The best performance achieved is 12.67% BLEU score for English to Hinglish and 25.72% BLEU score for MSAEA to English.

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