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GCC AI Research

Challenges and Solutions in Developing Code-switched Arabic-English NLP Systems

MBZUAI · Notable

NLP Arabic AI Research ASR MT

Summary

Injy Hamed from NYU Abu Dhabi's CAMeL Lab presented work on Egyptian Arabic-English code-switching for ASR and MT. She discussed the ArzEn-ST speech translation corpus and compared end-to-end and hybrid systems for ASR. For MT, she presented data augmentation and word segmentation techniques to handle data scarcity, also addressing ASR evaluation challenges in code-switching. Why it matters: Research into code-switching is crucial for building NLP systems capable of processing real-world language use in the Arab world.

Keywords

code-switching · Arabic-English · NLP · ASR · machine translation

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