Abstract
Code-switching (CS) poses several challenges to NLP tasks, where data sparsity is a main problem hindering the development of CS NLP systems. In this paper, we investigate data augmentation techniques for synthesizing Dialectal Arabic-English CS text. We perform lexical replacements using parallel corpora and alignments where CS points are either randomly chosen or learnt using a sequence-to-sequence model. We evaluate the effectiveness of data augmentation on language modeling (LM), machine translation (MT), and automatic speech recognition (ASR) tasks. Results show that in the case of using 1-1 alignments, using trained predictive models produces more natural CS sentences, as reflected in perplexity. By relying on grow-diag-final alignments, we then identify aligning segments and perform replacements accordingly. By replacing segments instead of words, the quality of synthesized data is greatly improved. With this improvement, random-based approach outperforms using trained predictive models on all extrinsic tasks. Our best models achieve 33.6% improvement in perplexity, +3.2-5.6 BLEU points on MT task, and 7% relative improvement on WER for ASR task. We also contribute in filling the gap in resources by collecting and publishing the first Arabic English CS-English parallel corpus.
Abstract (translated)
URL
https://arxiv.org/abs/2205.12649
