Data Augmentation Techniques for Machine Translation of Code-Switched
  Texts: A Comparative Study
Injy Hamed Nizar Habash Ngoc Thang Vu
Code-switching (CSW) text generation has been receiving increasing attention
as a solution to address data scarcity. In light of this growing interest, we
need more comprehensive studies comparing different augmentation approaches. In
this work, we compare three popular approaches: lexical replacements,
linguistic theories, and back-translation (BT), in the context of Egyptian
Arabic-English CSW. We assess the effectiveness of the approaches on machine
translation and the quality of augmentations through human evaluation. We show
that BT and CSW predictive-based lexical replacement, being trained on CSW
parallel data, perform best on both tasks. Linguistic theories and random
lexical replacement prove to be effective in the lack of CSW parallel data,
where both approaches achieve similar results.