Benchmarking Dialectal Arabic|English Machine Translation

Benchmarking Dialectal Arabic-English Machine Translation

While a number of research efforts target the former, the unavailability of evaluation benchmarks remain a major hindrance in tracking the progress in low- ...

AraBench: Benchmarking Dialectal Arabic-English Machine ...

We report strong baselines using several training settings: fine-tuning, back-translation and data augmentation. The evaluation suite opens a wide range of ...

Benchmarking Dialectal Arabic-English Machine Translation

AraBench: Benchmarking Dialectal Arabic-English Machine Translation. Hassan Sajjad Ahmed Abdelali Nadir Durrani Fahim Dalvi. {hsajjad,abdelali ...

AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs

We address this gap by introducing seven synthetic datasets in dialects alongside Modern Standard Arabic (MSA), created using Machine ...

AraBench: Benchmarking Dialectal Arabic-English Machine ...

Apart from machine translation where researchers are starting to propose benchmarks such as AraBench (Sajjad et al., 2020) , there are no benchmarks that can be ...

Benchmarking Dialectal Arabic-English Machine Translation

The introduction of AraBench, an evaluation suite for dialectal Arabic to English machine translation, which pool together already available ...

Benchmarking Dialectal Arabic-English Machine Translation

The evaluation suite opens a wide range of research frontiers to push efforts in low-resource machine translation, particularly Arabic dialect translation. The ...

AraBench: Benchmarking Dialectal Arabic-English Machine ...

originals · AraBench: Benchmarking Dialectal Arabic-English Machine Translation · Speakers · Hassan Sajjad · Hadeel Saadany · Downloads · Next from ...

arXiv:2305.14989v2 [cs.CL] 24 Oct 2023

AraBench: Benchmarking · dialectal Arabic-English machine translation. In. Proceedings of the 28th International Conference on Computational ...

AraBench: Benchmarking Dialectal Arabic-English ... - Hassan Sajjad

AraBench: Benchmarking Dialectal Arabic-English Machine Translation. Hassan Sajjad, Ahmed Abdelali, Nadir Durrani, Fahim Dalvi. December 2020. PDF Cite. Type.

Benchmarking Dialectal Arabic-Turkish Machine Translation

We evaluate the quality of the translations and report the results achieved. We make this first-of-a-kind data set publicly available to support research in ...

(PDF) Benchmarking LLaMA-3 on Arabic Language Generation Tasks

additional MSA-English sentences. Code-Switching. This task cluster addresses. translating Arabic dialect ...

A Survey on Machine Translation of Low-Resource Arabic Dialects

Abstract: For many years, machine translation has been one of the most studied topics for AI researchers, this initially started with simple and traditional ...

AraBench: Benchmarking Dialectal Arabic-English Machine ...

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Benchmarking Dialectal Arabic-Turkish Machine Translation

We evaluate the quality of the translations and report the results achieved. We make this first-of-a-kind data set publicly available to support research in ...

Neural Machine Translation of Dialectal-Dialectal Arabic

Most of the research in Arabic dialects. MT primarily focuses on translating dialects into either MSA or English. Very few studies explore ...

Elissa: A Dialectal to Standard Arabic Machine Translation System

Elissa is presented, a machine translation (MT) system from DA to MSA that employs a rule-based approach that relies on morphological analysis, ...

Machine translation for Arabic dialects (survey) - ScienceDirect.com

We focus, in this paper, on machine translation in the context of Arabic dialects. We provide a survey of recent research in this area.

Machine Translation of Arabic Dialects - UPenn CIS

Previous research on Dialectal. Arabic MT has focused on normalizing dialectal in- put words into MSA equivalents before translating to English, and they deal ...

FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation

AraBench: Benchmarking dialectal Arabic-English machine translation . In. Proceedings of the 28th International Conference on Computational ...