Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text

TL;DR

This study evaluates various segmentation techniques for neural machine translation of code-switched Egyptian Arabic-English text, revealing that segmentation choice impacts translation quality differently based on data resource levels.

Contribution

It systematically compares morphology-based and frequency-based segmentation methods for code-switched MT, highlighting their effectiveness under different data conditions.

Findings

Morphology-aware segmenters excel in segmentation accuracy.

Frequency-based segmentation performs well in translation tasks.

Combined segmentation methods are beneficial in low-resource scenarios.

Abstract

Data sparsity is one of the main challenges posed by code-switching (CS), which is further exacerbated in the case of morphologically rich languages. For the task of machine translation (MT), morphological segmentation has proven successful in alleviating data sparsity in monolingual contexts; however, it has not been investigated for CS settings. In this paper, we study the effectiveness of different segmentation approaches on MT performance, covering morphology-based and frequency-based segmentation techniques. We experiment on MT from code-switched Arabic-English to English. We provide detailed analysis, examining a variety of conditions, such as data size and sentences with different degrees of CS. Empirical results show that morphology-aware segmenters perform the best in segmentation tasks but under-perform in MT. Nevertheless, we find that the choice of the segmentation setup to use for MT is highly dependent on the data size. For extreme low-resource scenarios, a combination of frequency and morphology-based segmentations is shown to perform the best. For more resourced settings, such a combination does not bring significant improvements over the use of frequency-based segmentation.