Large Language Models (LLMs) have shown promising results in dialectal machine translation, yet the impact of explicit linguistic features remains underexplored. This paper examines whether part-of-speech (POS) tags and diacritization help or hinder LLM-based translation between Algerian dialect (Darija) and Modern Standard Arabic (MSA). Using a linguistically enriched subset of the PADIC dataset, we conduct bidirectional experiments across several frontier and open-weight LLMs, evaluated with automatic metrics and human judgments of adequacy and fluency. Results reveal a dual and asymmetric effect: diacritics can improve adequacy in the MSA → Algerian dialect direction, while POS tags and forced diacritization often introduce noise, especially for Algerian dialect → MSA translation. We further observe a mismatch between traditional overlap-based metrics and human evaluation, suggesting limitations in current evaluation practices. Overall, explicit linguistic augmentation does not consistently benefit LLM-based dialectal translation and must be applied cautiously.

Named Entity Recognition (NER) is a crucial task within natural language processing (NLP) that entails the identification and classification of entities, such as person, organization and location. This study delves into NER specifically in the Arabic language, focusing on the Algerian dialect. While previous research in NER has primarily concentrated on Modern Standard Arabic (MSA), the advent of social media has prompted a need to address the variations found in different Arabic dialects. Moreover, given the notable achievements of Large-scale pre-trained models (PTMs) based on the BERT architecture, this paper aims to evaluate Arabic pre-trained models using an Algerian dataset that covers different domains and writing styles. Additionally, an error analysis is conducted to identify PTMs’ limitations, and an investigation is carried out to assess the performance of trained MSA models on the Algerian dialect. The experimental results and subsequent analysis shed light on the complexities of NER in Arabic, offering valuable insights for future research endeavors.