  Data sparsity is a main problem hindering the development of code-switching
(CS) NLP systems. In this paper, we investigate data augmentation techniques
for synthesizing dialectal Arabic-English CS text. We perform lexical
replacements using word-aligned parallel corpora where CS points are either
randomly chosen or learnt using a sequence-to-sequence model. We compare these
approaches against dictionary-based replacements. We assess the quality of the
generated sentences through human evaluation and evaluate the effectiveness of
data augmentation on machine translation (MT), automatic speech recognition
(ASR), and speech translation (ST) tasks. Results show that using a predictive
model results in more natural CS sentences compared to the random approach, as
reported in human judgements. In the downstream tasks, despite the random
approach generating more data, both approaches perform equally (outperforming
dictionary-based replacements). Overall, data augmentation achieves 34%
improvement in perplexity, 5.2% relative improvement on WER for ASR task,
+4.0-5.1 BLEU points on MT task, and +2.1-2.2 BLEU points on ST over a baseline
trained on available data without augmentation.
