The AMARA Corpus: Building Parallel Language Resources for the Educational Domain

Ahmed Abdelali, Francisco Guzman, Hassan Sajjad, Stephan Vogel


Abstract
This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.
Anthology ID:
L14-1675
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1856–1862
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/877_Paper.pdf
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/877_Paper.pdf