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Curriculum Learning and Pseudo-Labeling Improve the Generalization of Multi-Label Arabic Dialect Identification Models

arXiv:2602.12937v1 Announce Type: new Abstract: Being modeled as a single-label classification task for a long time, recent work has argued that Arabic Dialect Identification (ADI) should be framed as a multi-label classification task. However, ADI remains constrained by the availability of single-label datasets, with no large-scale multi-label resources available for training. By analyzing models trained on single-label ADI data, we show that the main difficulty in repurposing such datasets for Multi-Label Arabic Dialect Identification (MLADI) lies in the selection of negative samples, as many sentences treated as negative could be acceptable in multiple dialects. To address these issues, we construct a multi-label dataset by generating automatic multi-label annotations using GPT-4o and binary dialect acceptability classifiers, with aggregation guided by the Arabic Level of Dialectness (ALDi). Afterward, we train a BERT-based multi-label classifier using curriculum learning strategie

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Ali Mekky, Mohamed El Zeftawy, Lara Hassan, Amr Keleg, Preslav Nakov · · 1 min read · 5 views
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arXiv:2602.12937v1 Announce Type: new Abstract: Being modeled as a single-label classification task for a long time, recent work has argued that Arabic Dialect Identification (ADI) should be framed as a multi-label classification task. However, ADI remains constrained by the availability of single-label datasets, with no large-scale multi-label resources available for training. By analyzing models trained on single-label ADI data, we show that the main difficulty in repurposing such datasets for Multi-Label Arabic Dialect Identification (MLADI) lies in the selection of negative samples, as many sentences treated as negative could be acceptable in multiple dialects. To address these issues, we construct a multi-label dataset by generating automatic multi-label annotations using GPT-4o and binary dialect acceptability classifiers, with aggregation guided by the Arabic Level of Dialectness (ALDi). Afterward, we train a BERT-based multi-label classifier using curriculum learning strategies aligned with dialectal complexity and label cardinality. On the MLADI leaderboard, our best-performing LAHJATBERT model achieves a macro F1 of 0.69, compared to 0.55 for the strongest previously reported system. Code and data are available at https://mohamedalaa9.github.io/lahjatbert/.

Executive Summary

The article presents a novel approach to Multi-Label Arabic Dialect Identification (MLADI) by addressing the limitations of existing single-label datasets. The authors construct a multi-label dataset using GPT-4o and binary dialect acceptability classifiers, guided by the Arabic Level of Dialectness (ALDi). They employ curriculum learning strategies to train a BERT-based multi-label classifier, achieving a significant improvement in macro F1 score compared to previous systems. The study highlights the importance of selecting appropriate negative samples and leveraging advanced language models for dataset construction.

Key Points

  • ▸ ADI has traditionally been treated as a single-label classification task, but recent work suggests a multi-label approach is more appropriate.
  • ▸ The main challenge in MLADI is the selection of negative samples, as many sentences could be acceptable in multiple dialects.
  • ▸ The authors construct a multi-label dataset using GPT-4o and binary dialect acceptability classifiers, guided by ALDi.
  • ▸ Curriculum learning strategies aligned with dialectal complexity and label cardinality are used to train a BERT-based multi-label classifier.
  • ▸ The best-performing model, LAHJATBERT, achieves a macro F1 score of 0.69, significantly higher than the previous best of 0.55.

Merits

Innovative Approach

The article introduces a novel method for constructing multi-label datasets using advanced language models and binary classifiers, which is a significant advancement in the field of Arabic Dialect Identification.

Significant Improvement

The proposed model achieves a substantial improvement in macro F1 score, demonstrating the effectiveness of the curriculum learning strategies and the constructed dataset.

Comprehensive Analysis

The study provides a thorough analysis of the challenges in MLADI and offers a well-reasoned solution, backed by empirical evidence.

Demerits

Limited Dataset

The constructed multi-label dataset, while innovative, is still limited in size and scope compared to large-scale single-label datasets, which could impact the generalizability of the findings.

Dependency on Advanced Models

The method relies heavily on the use of GPT-4o and other advanced models, which may not be accessible to all researchers, limiting the reproducibility of the results.

Complexity of Curriculum Learning

The curriculum learning strategies, while effective, add complexity to the training process, which might be challenging to implement and optimize for other researchers.

Expert Commentary

The article presents a significant advancement in the field of Arabic Dialect Identification by addressing the limitations of single-label datasets and proposing a multi-label approach. The use of GPT-4o and binary dialect acceptability classifiers to construct a multi-label dataset is innovative and demonstrates the potential of advanced language models in handling complex linguistic tasks. The application of curriculum learning strategies further enhances the model's performance, achieving a notable improvement in macro F1 score. However, the study's reliance on advanced models and the complexity of curriculum learning strategies may pose challenges for reproducibility and implementation. Despite these limitations, the findings contribute valuable insights to the broader discussion on multilingual language models, data annotation, and curriculum learning in NLP. The practical and policy implications of this research underscore the need for more comprehensive datasets and investment in advanced technologies to support research in underrepresented languages.

Recommendations

  • ✓ Future research should explore the use of other advanced language models and techniques for constructing multi-label datasets, ensuring the reproducibility and scalability of the proposed method.
  • ✓ Investigating the impact of different curriculum learning strategies on model performance could provide further insights into optimizing training processes for multi-label classification tasks.
  • ✓ Expanding the constructed multi-label dataset to include more dialects and linguistic variations would enhance the generalizability of the findings and contribute to the broader field of Arabic Dialect Identification.

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