The study introduces AraSpider, the first Arabic version of the Spider dataset, to advance Arabic NLP. Four multilingual translation models and two text-to-SQL models (ChatGPT 3.5 and SQLCoder) were evaluated. Back translation significantly improved the performance of both ChatGPT 3.5 and SQLCoder on the AraSpider dataset. Why it matters: This work democratizes access to text-to-SQL resources for Arabic speakers and provides a methodology for translating datasets to other languages.
The paper introduces Ara-HOPE, a human-centric post-editing evaluation framework for Dialectal Arabic to Modern Standard Arabic (DA-MSA) translation. Ara-HOPE includes a five-category error taxonomy and a decision-tree annotation protocol designed to address the challenges of dialect-specific MT errors. Evaluation of Jais, GPT-3.5, and NLLB-200 shows dialect-specific terminology and semantic preservation remain key challenges. Why it matters: The new framework and public dataset will help improve the evaluation and development of dialect-aware MT systems for Arabic.
This paper explores Dialectal Arabic (DA) to Modern Standard Arabic (MSA) machine translation using prompting and fine-tuning techniques for Levantine, Egyptian, and Gulf dialects. The study found that few-shot prompting outperformed zero-shot and chain-of-thought methods across six large language models, with GPT-4o achieving the highest performance. A quantized Gemma2-9B model achieved a chrF++ score of 49.88, outperforming zero-shot GPT-4o (44.58). Why it matters: The research provides a resource-efficient pipeline for DA-MSA translation, enabling more inclusive language technologies by addressing the challenges posed by dialectal variations in Arabic.
This paper describes the QCRI-Columbia-NYUAD group's Egyptian Arabic-to-English statistical machine translation system submitted to the NIST OpenMT'2015 competition. The system used tools like 3arrib and MADAMIRA for processing and standardizing informal dialectal Arabic. The system was trained using phrase-based SMT with features such as operation sequence model, class-based language model and neural network joint model. Why it matters: The work demonstrates advances in machine translation for dialectal Arabic, a challenging but important area for regional communication and NLP research.
This paper introduces SimulMask, a new paradigm for fine-tuning large language models (LLMs) for simultaneous translation. SimulMask utilizes a novel attention masking approach that models simultaneous translation during fine-tuning by masking attention for a desired decision policy. Applied to a Falcon LLM on the IWSLT 2017 dataset, SimulMask achieves improved translation quality compared to state-of-the-art prompting optimization strategies across five language pairs while reducing computational cost. Why it matters: The proposed method offers a more efficient way to adapt LLMs for real-time translation, potentially enhancing multilingual communication tools and services.