Year: 2023
Type: conference-paper
Abstract: Low-resource Machine Translation (MT) is characterized by the scarce availability of training data and/or standardized evaluation benchmarks.In the context of Dialectal Arabic, recent works introduced several evaluation benchmarks covering both Modern Standard Arabic (MSA) and dialects, mapping, however, mostly to a single Indo-European language -English.In this work, we introduce a multi-lingual corpus consisting of 120,600 multi-parallel sentences in English, French, German, Greek, Spanish, and MSA selected from the OpenSubtitles corpus (Lison et al., 2018), which were manually translated into the North Levantine Arabic.By conducting a series of training and fine-tuning experiments, we explore how this novel resource can contribute to the research on Arabic MT.We make the dataset publicly available at http://hdl.handle.net/11234/1-5033for research purposes.
Institution: Charles University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389518387