Revisiting Common Assumptions about Arabic Dialects in NLP
This work addresses a foundational issue for researchers and practitioners in Arabic NLP by challenging common assumptions, though it is incremental as it builds on existing datasets and analyses.
The paper tackles the problem of unverified assumptions about Arabic dialects in NLP, such as grouping them into distinguishable regional dialects, by analyzing a multi-label dataset with manual assessments from speakers of 11 country-level dialects, finding that these assumptions oversimplify reality and are not always accurate, which may hinder progress in Arabic NLP tasks.
Arabic has diverse dialects, where one dialect can be substantially different from the others. In the NLP literature, some assumptions about these dialects are widely adopted (e.g., ``Arabic dialects can be grouped into distinguishable regional dialects") and are manifested in different computational tasks such as Arabic Dialect Identification (ADI). However, these assumptions are not quantitatively verified. We identify four of these assumptions and examine them by extending and analyzing a multi-label dataset, where the validity of each sentence in 11 different country-level dialects is manually assessed by speakers of these dialects. Our analysis indicates that the four assumptions oversimplify reality, and some of them are not always accurate. This in turn might be hindering further progress in different Arabic NLP tasks.