Year: 2016
Type: conference-paper
Abstract: Statistical Machine Translation (SMT) relies on the availability of rich parallel corpora.However, in the case of under-resourced languages or some specific domains, parallel corpora are not readily available.This leads to under-performing machine translation systems in those sparse data settings.To overcome the low availability of parallel resources the machine translation community has recognized the potential of using comparable resources as training data.However, most efforts have been related to European languages and less in middle-east languages.In this study, we report comparable corpora created from news articles for the pair English -{Arabic, Persian, Urdu} languages.The data has been collected over a period of a year, entails Arabic, Persian and Urdu languages.Furthermore using the English as a pivot language, comparable corpora that involve more than one language can be created, e.g.English-Arabic -Persian, English -Arabic -Urdu, English -Urdu -Persian, etc. Upon request the data can be provided for research purposes.
Authors: Murad Abouammoh, Kashif Shah, Ahmet Aker
Institutions: University of Sheffield, King Saud University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2577320159