Advancing Dialectal Arabic to Modern Standard Arabic Machine Translation

• URL: http://arxiv.org/abs/2507.20301v2
• Date: Tue, 02 Sep 2025 20:29:50 GMT
• Title: Advancing Dialectal Arabic to Modern Standard Arabic Machine Translation
• Authors: Abdullah Alabdullah, Lifeng Han, Chenghua Lin,
• Abstract summary: This paper presents two core contributions to advancing DA-MSA translation for the Levantine, Egyptian, and Gulf dialects.<n>Few-shot prompting consistently outperformed zero-shot, chain-of-thought, and our proposed Ara-TEaR method.<n>For fine-tuning LLMs, a quantized Gemma2-9B model achieved a chrF++ score of 49.88, outperforming zero-shot GPT-4o (44.58)
• Score: 22.369277951685234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialectal Arabic (DA) poses a persistent challenge for natural language processing (NLP), as most everyday communication in the Arab world occurs in dialects that diverge significantly from Modern Standard Arabic (MSA). This linguistic divide impedes progress in Arabic machine translation. This paper presents two core contributions to advancing DA-MSA translation for the Levantine, Egyptian, and Gulf dialects, particularly in low-resource and computationally constrained settings: (i) a comprehensive evaluation of training-free prompting techniques, and (ii) the development of a resource-efficient fine-tuning pipeline. Our evaluation of prompting strategies across six large language models (LLMs) found that few-shot prompting consistently outperformed zero-shot, chain-of-thought, and our proposed Ara-TEaR method. Ara-TEaR is designed as a three-stage self-refinement prompting process, targeting frequent meaning-transfer and adaptation errors in DA-MSA translation. In this evaluation, GPT-4o achieved the highest performance across all prompting settings. For fine-tuning LLMs, a quantized Gemma2-9B model achieved a chrF++ score of 49.88, outperforming zero-shot GPT-4o (44.58). Joint multi-dialect trained models outperformed single-dialect counterparts by over 10% chrF++, and 4-bit quantization reduced memory usage by 60% with less than 1% performance loss. The results and insights of our experiments offer a practical blueprint for improving dialectal inclusion in Arabic NLP, showing that high-quality DA-MSA machine translation is achievable even with limited resources and paving the way for more inclusive language technologies.

Related papers

• ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning [0.0]
  ALPS (Arabic Linguistic & Pragmatic Suite) is a native, expert-curated diagnostic challenge set probing Deep Semantics and Pragmatics.<n> ALPS targets the depth of linguistic understanding through 531 rigorously crafted questions across 15 tasks and 47 subtasks.<n>We developed the dataset with deep expertise in Arabic linguistics, guaranteeing cultural authenticity and eliminating translation artifacts.
  arXiv  Detail & Related papers  (2026-02-19T03:51:37Z)
• Doing More with Less: Data Augmentation for Sudanese Dialect Automatic Speech Recognition [0.0]
  This paper presents a study of data augmentation techniques for fine-tuning OpenAI Whisper models.<n>It establishes the first benchmark for the Sudanese dialect.
  arXiv  Detail & Related papers  (2026-01-11T08:28:31Z)
• DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models [54.10223256792762]
  We present DialectalArabicMMLU, a new benchmark for evaluating the performance of large language models (LLMs) across Arabic dialects.<n>We extend the MMLU-Redux framework through manual translation and adaptation of 3K multiple-choice question-answer pairs into five major dialects.
  arXiv  Detail & Related papers  (2025-10-31T15:17:06Z)
• DialectGen: Benchmarking and Improving Dialect Robustness in Multimodal Generation [111.94720088481614]
  Can multimodal generative models effectively produce content given dialectal textual input?<n>We construct a new large-scale benchmark spanning six common English dialects.<n>We design a general encoder-based mitigation strategy for multimodal generative models.
  arXiv  Detail & Related papers  (2025-10-16T17:56:55Z)
• BALSAM: A Platform for Benchmarking Arabic Large Language Models [34.50348949235453]
  BALSAM is a comprehensive, community-driven benchmark aimed at advancing Arabic LLM development and evaluation.<n>It includes 78 NLP tasks from 14 broad categories, with 52K examples divided into 37K test and 15K development, and a centralized, transparent platform for blind evaluation.
  arXiv  Detail & Related papers  (2025-07-30T12:16:39Z)
• A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context [0.9074663948713616]
  Mental health disorders pose a growing public health concern in the Arab world.<n>This study comprehensively evaluates 8 large language models (LLMs) on diverse mental health datasets.
  arXiv  Detail & Related papers  (2025-01-12T16:17:25Z)
• SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment [78.4550589538805]
  We propose an efficient multilingual reasoning alignment approach that precisely identifies and fine-tunes the layers responsible for handling multilingualism.<n> Experimental results show that our method, SLAM, only tunes 6 layers' feed-forward sub-layers including 6.5-8% of all parameters within 7B and 13B LLMs.
  arXiv  Detail & Related papers  (2025-01-07T10:29:43Z)
• Resource-Aware Arabic LLM Creation: Model Adaptation, Integration, and Multi-Domain Testing [0.0]
  This paper presents a novel approach to fine-tuning the Qwen2-1.5B model for Arabic language processing using Quantized Low-Rank Adaptation (QLoRA) on a system with only 4GB VRAM.<n>We detail the process of adapting this large language model to the Arabic domain, using diverse datasets including Bactrian, OpenAssistant, and Wikipedia Arabic corpora.<n> Experimental results over 10,000 training steps show significant performance improvements, with the final loss converging to 0.1083.
  arXiv  Detail & Related papers  (2024-12-23T13:08:48Z)
• Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion [55.27025066199226]
  This paper addresses the need for democratizing large language models (LLM) in the Arab world.<n>One practical objective for an Arabic LLM is to utilize an Arabic-specific vocabulary for the tokenizer that could speed up decoding.<n>Inspired by the vocabulary learning during Second Language (Arabic) Acquisition for humans, the released AraLLaMA employs progressive vocabulary expansion.
  arXiv  Detail & Related papers  (2024-12-16T19:29:06Z)
• Enhancing Low-Resource NMT with a Multilingual Encoder and Knowledge Distillation: A Case Study [14.300310437948443]
  This paper explores a framework that leverages the benefits of a pre-trained language model along with knowledge distillation in a seq2seq architecture to facilitate translation for low-resource languages. Our framework is evaluated on three low-resource Indic languages in four Indic-to-Indic directions, yielding significant BLEU-4 and chrF improvements over baselines.
  arXiv  Detail & Related papers  (2024-07-09T04:19:52Z)
• CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models [53.9835961434552]
  We introduce the Chinese Instruction-Following Benchmark (CIF-Bench) to evaluate the generalizability of large language models (LLMs) to the Chinese language. CIF-Bench comprises 150 tasks and 15,000 input-output pairs, developed by native speakers to test complex reasoning and Chinese cultural nuances. To mitigate data contamination, we release only half of the dataset publicly, with the remainder kept private, and introduce diversified instructions to minimize score variance.
  arXiv  Detail & Related papers  (2024-02-20T16:02:12Z)
• ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic [51.922112625469836]
  We present datasetname, the first multi-task language understanding benchmark for the Arabic language. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models.
  arXiv  Detail & Related papers  (2024-02-20T09:07:41Z)
• Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations [59.056367787688146]
  This paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. We construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages. By utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages.
  arXiv  Detail & Related papers  (2023-10-31T08:09:20Z)
• AceGPT, Localizing Large Language Models in Arabic [73.39989503874634]
  The paper proposes a comprehensive solution that includes pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities.
  arXiv  Detail & Related papers  (2023-09-21T13:20:13Z)
• ChatGPT for Arabic Grammatical Error Correction [5.945320097465418]
  Large language models (LLMs) fine-tuned to follow human instruction have exhibited significant capabilities in English NLP tasks. In this paper, we delve into abilities of instruction fine-tuned LLMs in Arabic GEC, a task made complex due to Arabic's rich morphology. We find that instruction fine-tuned models, regardless of their size, significantly underperform compared to fully fine-tuned models of significantly smaller sizes.
  arXiv  Detail & Related papers  (2023-08-08T18:00:39Z)
• Strategies for improving low resource speech to text translation relying on pre-trained ASR models [59.90106959717875]
  This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST) We conducted experiments on both simulated and real-low resource setups, on language pairs English - Portuguese, and Tamasheq - French respectively.
  arXiv  Detail & Related papers  (2023-05-31T21:58:07Z)
• LAraBench: Benchmarking Arabic AI with Large Language Models [26.249084464525044]
  LAraBench addresses this gap for Arabic Natural Language Processing (NLP) and Speech Processing tasks. We utilize models such as GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM to tackle 33 distinct tasks across 61 publicly available datasets. This involved 98 experimental setups, encompassing 296K data points, 46 hours of speech, and 30 sentences for Text-to-Speech (TTS)
  arXiv  Detail & Related papers  (2023-05-24T10:16:16Z)
• No Language Left Behind: Scaling Human-Centered Machine Translation [69.28110770760506]
  We create datasets and models aimed at narrowing the performance gap between low and high-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-11T07:33:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.