Estimating the Level of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasets (2405.11282v3)

Abstract: On annotating multi-dialect Arabic datasets, it is common to randomly assign the samples across a pool of native Arabic speakers. Recent analyses recommended routing dialectal samples to native speakers of their respective dialects to build higher-quality datasets. However, automatically identifying the dialect of samples is hard. Moreover, the pool of annotators who are native speakers of specific Arabic dialects might be scarce. Arabic Level of Dialectness (ALDi) was recently introduced as a quantitative variable that measures how sentences diverge from Standard Arabic. On randomly assigning samples to annotators, we hypothesize that samples of higher ALDi scores are harder to label especially if they are written in dialects that the annotators do not speak. We test this by analyzing the relation between ALDi scores and the annotators' agreement, on 15 public datasets having raw individual sample annotations for various sentence-classification tasks. We find strong evidence supporting our hypothesis for 11 of them. Consequently, we recommend prioritizing routing samples of high ALDi scores to native speakers of each sample's dialect, for which the dialect could be automatically identified at higher accuracies.

• The paper demonstrates that higher ALDi scores significantly correlate with lower interannotator agreement for non-dialect identification tasks.
• It evaluates 15 multi-dialect Arabic datasets across diverse tasks, highlighting the need for task assignments based on annotator dialect proficiency.
• Key implications include improved annotation strategies through strategic task routing and advanced automatic dialect identification to enhance dataset quality.

Estimating the Level of Dialectness and Its Impact on Interannotator Agreement in Multi-dialect Arabic Datasets

The paper explores the relationship between dialectal variation in the Arabic language and the challenges it poses to NLP system annotation efforts. Specifically, it investigates how the "Arabic Level of Dialectness" (ALDi), a quantitative measure indicating the degree of divergence from Modern Standard Arabic (MSA), impacts inter-annotator agreement in multi-dialect Arabic datasets.

Dialectal Arabic (DA) presents a multilayered challenge within NLP due to its lack of mutual intelligibility across various regional dialects and its divergence from MSA, which serves as a standardized linguistic variant used in formal contexts. Annotators, traditionally recruited without regard to their dialectal backgrounds, have often faced difficulties comprehending and labeling samples from dialects they are unfamiliar with. Previous works have suggested aligning dataset samples with annotators who are native speakers of the respective dialects to enhance labeling accuracy, a recommendation that has been constrained by practical challenges, including the scarcity of native annotators and the difficulty in performing automatic dialect identification of text samples.

In light of these challenges, ALDi scores offer a potential pathway to improve annotation methodologies. By quantifying the spectrum of dialectical divergence from MSA, ALDi enables a nuanced approach to assign annotation tasks based on the dialectical nature of text samples. The hypothesis examined is that samples with higher ALDi scores are more likely to yield lower inter-annotator agreement, especially when annotated by individuals not native to the dialect in question.

The paper evaluates this hypothesis across 15 datasets labeled for diverse classification tasks, including offensive text classification, sentiment analysis, speech act detection, and dialect identification. Findings reflect a strong negative correlation between higher ALDi scores and inter-annotator agreement across most non-DI tasks. The lower agreement for texts with high ALDi scores suggests increased difficulty in annotation due to reduced comprehensibility. Interestingly, for dialect identification tasks, higher ALDi scores led to greater annotator agreement, likely due to the distinctiveness of strong dialect cues aiding dialect identification.

The methodological implications urge dataset creators to leverage ALDi scores for assigning annotation tasks more strategically. By routing low-ALDi samples, which remain close to MSA and are thus comprehensible to most Arabic speakers, efficiently across a broad annotator pool, while assigning high-ALDi samples specifically to annotators fluent in the respective dialects, annotation accuracy can potentially improve without excessively increasing resource demands.

Furthermore, this analysis delineates a future research trajectory focused on integrating advanced automatic dialect identification methods that could further streamline this process. Addressing the imbalance in annotator availability across different Arabic dialects might also necessitate innovative crowd-sourcing or machine learning solutions.

In conclusion, ALDi presents a promising tool for refining dataset development methodologies in Arabic NLP, offering a path to harmonize inter-annotator agreement, enhance dataset quality, and address complex dialectal challenges intrinsic to Arabic. This work also opens pathways for analogous applications in other linguistically diverse or dialectically complex languages.