Active Learning for Multidialectal A rabic POS Tagging (2025)

Abstract: Multidialectal Arabic POS tagging is challenging due to the morphological richness and high variability among dialects. While POS tagging for MSA has advanced thanks to the availability of annotated datasets, creating similar resources for dialects remains costly and labor-intensive. Increasing the size of annotated datasets does not necessarily result in better performance. Active learning offers a more efficient alternative by prioritizing annotating the most informative samples. This paper proposes an active learning approach for multidialectal Arabic POS tagging. Our experiments revealed that annotating approximately 15,000 tokens is sufficient for high performance. We further demonstrate that using a fine-tuned model from one dialect to guide the selection of initial samples from another dialect accelerates convergence—reducing the annotation requirement by about 2,000 tokens. In conclusion, we propose an active learning pipeline and demonstrate that, upon reaching its defined stopping point of 16,000 annotated tokens, it achieves an accuracy of 97.6% on the Emirati Corpus.

## introduction pos tagging assigns a part of speech to each word in a sequence. it is a crucial step in various nlp tasks such as named entity recognition (ner) @xcite @xcite @xcite , machine translation (yazar and kiliç, 2025), text summarization @xcite , word sense disambiguation @xcite , and synonymous extraction @xcite . in arabic, pos tagging is particularly challenging due to its highly inflectional morphology across msa and dialects @xcite . first, arabic's inflectional and derivational nature leads to numerous affixes that modify stems @xcite . second, dialectal variation adds complexity, as an example, different affixes for the progressive tense-e.g., ( / b ) in levantine and egyptian, ( / d ) in iraqi, and ( / k ) in moroccan. third, each dialect includes unique vocabulary absent in msa and other dialects (hamed et al., 2025; jarrar et al., 2023c), complicating pos tagging further. the importance of pos tagging, combined with the complexity of arabic morphology, has motivated extensive research aimed at improving pos tagging accuracy, coverage, and speed. approaches differ in datasets, tag sets, and nlp techniques. recent methods have achieved strong results on msa, supported by a large morphologically annotated corpus @xcite . research on pos tagging for arabic dialects is still in its early stages and has yet to produce a tagger that covers most dialects @xcite . this is due to morphological challenges specific to dialects, as discussed earlier, and the lack of morphologically annotated corpora. creating such corpora for each dialect is costly. active learning approaches, which prioritize annotating the most informative and representative data samples @xcite , effectively reduce annotation cost and effort while maintaining or improving model performance. studies show that it can cut labeling effort by up to 50% @xcite , and recent work demonstrates strong results in many domains: 40% labeled data sufficed in document image classification @xcite , 20.83%-24.34% in retail recognition achieved 95% performance @xcite , and over 90% time reduction was reported in biomedical analysis @xcite . thus, active learning provides a costeffective method for developing multidialectal pos taggers by producing high-quality morphologically annotated corpora with minimal annotation. this paper evaluates active learning for pos tagging of four arabic dialects (palestinian, syrian, egyptian, and emirati) along with msa. the evaluation examines four key factors affecting active learning cycle (see figure 1 ): learning models, initial sample selection methods, annotation levels, and query strategies. results show that annotating approximately 15, 000 informative tokens from msa, palestinian, egyptian, and syrian corpora is sufficient to achieve high performance, reaching 97.3%, 95.5%, 94.8%, and 92.9%, respectively. it also explores using fine-tuned models from other dialects for initial sample selection, showing a 2.5-5% accuracy gain in the first iteration and faster convergence. finally, it introduces an active learning pipeline applicable to any unannotated dialect. in summary, our contributions are: the paper is structured as: section 2 reviews related work; sections 3 and 4 describe the corpora and the evaluation methodology; sections 5 and 6 present results and findings; section 7 proposes an active learning pipeline; and we conclude in section 8. ## related work various methods have been used for arabic and dialectal pos tagging, evolving from limited ruleand database-based approaches @xcite @xcite , through classical machine learning models @xcite @xcite , to neural networks @xcite @xcite , with recent advancements focusing on transformer-based models that achieved state-of-the-art results @xcite . all of the aforementioned machine learning and deep learning approaches rely on passive learning, where a large dataset is required for training. as a result, most msa approaches relied on one or more versions of the arabic treebank @xcite , which is sourced from different arabic news sources. the pos annotation procedure used in the arabic treebank (atb) involves segmenting raw input, applying the buckwalter morphological analyzer @xcite to generate candidate tags, and having human annotators select the correct pos. review passes follow to correct errors and ensure quality. while automatic analysis helps improve efficiency, substantial manual correction is needed, making the process time-consuming and reliant on trained annotators. similarly, the egyptian arabic treebank (arztb) @xcite , which is sourced from various egyptian informal data sources (discussion forums, text messaging, and chat), was built using the same approach. for the levantine dialects, the first morphologically annotated corpus was the palestinian corpus curras @xcite @xcite , annotated using the diwan tool @xcite , which integrated outputs from madamira @xcite and was completed by two annotators over one year. the lebanese corpus baladi @xcite was manually annotated by four annotators over ten months using annosheet, a google sheetsbased tool with javascript aids and curras-based suggestions. the syrian corpus nabra @xcite adopted this methodology, using the tawseem portal for annotation with smart features and was completed by nine annotators in one year. on the other hand, lisan @xcite , which cover multiple dialects, followed the same annotation methodology as curras, baladi, and nabra and was manually annotated using the adat tool by 35 annotators over two years, with separate teams per dialect. similarly, the gumar corpus @xcite used the madari tool for manual annotation by a dedicated team. all these corpora were sourced from diverse platforms such as facebook, twitter, blogs, forums, youtube, and tv shows. none of these corpora have utilized active learn-ing approaches in their annotation process, resulting in extended annotation timelines to produce corpora suitable for pos tagger training. active learning for pos tagging remains underexplored, with a notable recent study by @xcite applying it to six languages-excluding arabic. to our knowledge, this work is the first to apply active learning strategies for pos tagging to arabic and its dialects, demonstrating that a large annotated corpus is not required; annotating only the most informative tokens yields comparable results to training on the full dataset. ## datasets preparation and composition this paper investigates four arabic dialects-palestinian, syrian, egyptian, and emirati-alongside msa, using seven datasets annotated primarily with pos and other morphological features: (1) arabic treebank part 3 v3.2 (atb) @xcite , ( 2 ) salma @xcite , (3) bolt egyptian treebank (arztb) @xcite , ( 4 ) curras @xcite , ( 5 ) nabra @xcite , ( 6 ) baladi @xcite , ( 7 as the seven datasets use different pos tagsets, we employed a two-step unification process to align all corpora with the tagset used in alma @xcite , a lemmatizer and pos tagger for arabic that is part of the morphology module in sinatools @xcite . alma relies on the qabas lexicographic database @xcite , which links 110 lexicons @xcite and the arabic ontology @xcite -and 12 morphologically annotated corpora, including those 1 atb and arztb are licensed from the linguistic data consortium (ldc), while curras, baladi, nabra and salma are cc-by-4.0, and gumar is under a custom license used in addition to quranmorph @xcite . our two unification steps are: see full mapping list in after unifying the tags across all datasets, the datasets -except salma, which was used for testing only-were split following the methodology in (van der goot, 2021): train for training, tune ( 5% of train) for model selection, dev for initial evaluation, and test for final evaluation. ## active learning methodology this section presents the methodology for identifying the most effective query strategy to achieve high performance with minimal annotated data. to ensure fair comparison across query strategies, we first evaluated various pretrained learning models on arabic dialects from the literature to select the most suitable one (subsection 4.1). we then ran preliminary experiments to determine the best initial sample selection method (subsection 4.2). finally, with both components selected, we conducted experiments to identify the optimal query strategy (subsection 4.4). in our methodology, the active learning process continues until all tokens in each corpus's training set are annotated. this enables us to track the impact of each newly selected batch of tokens in each iteration and evaluate how increased annotation affects performance. ## evaluation results this section presents the evaluation results based on the methodology outlined in section 4, the selection of the best model (5.2), initial sample selection method (5.3), unselected token handling strategy (5.4), best query strategies (5.5), and use of a finetuned model from one dialect to select the initial sample in another (5.6). ## proposed active learning pipeline based on the evaluation in 5 and findings in 6, we propose an active learning pipeline for multidialectal arabic pos tagging on new unannotated datasets (see figure 1 ). the pipeline consists of three phases: (1) selecting the initial sample using a model fine-tuned on another dialect. (2) applying the breaking ties strategy to select 2,000 new tokens per iteration. (3) stopping once performance stabilizes. as discussed earlier, stabilization typically occurs at approximately 15, 000 tokens. therefore, the stopping criterion is set at 16, 000 tokens to ensure convergence. this pipeline was validated on the gumar (emirati) corpus, reaching a performance of 97.6% at stopping point. (see figure 16 ). figure 17: t-sne visualization of selected tokens distribution on the first eight iterations of the curras (palestinian) training set. notably, the last plot is for all selected tokens in eight iterations together. ## conclusion this paper evaluated using active learning for pos tagging in msa and arabic dialects-palestinian, syrian, and egyptian. selecting 15, 000 informative tokens per corpus proved sufficient for high per-formance. using a fine-tuned model from another dialect with the breaking ties strategy yielded the best results. thus, new dialectal pos taggers should adopt active learning to reduce annotation effort and cost.