Collecting and Using Comparable Corpora for Statistical Machine Translation

Inguna Skadiņa, Ahmet Aker, Nikos Mastropavlos, Fangzhong Su, Dan Tufis, Mateja Verlic, Andrejs Vasiļjevs, Bogdan Babych, Paul Clough, Robert Gaizauskas, Nikos Glaros, Monica Lestari Paramita, Mārcis Pinnis


Abstract
Lack of sufficient parallel data for many languages and domains is currently one of the major obstacles to further advancement of automated translation. The ACCURAT project is addressing this issue by researching methods how to improve machine translation systems by using comparable corpora. In this paper we present tools and techniques developed in the ACCURAT project that allow additional data needed for statistical machine translation to be extracted from comparable corpora. We present methods and tools for acquisition of comparable corpora from the Web and other sources, for evaluation of the comparability of collected corpora, for multi-level alignment of comparable corpora and for extraction of lexical and terminological data for machine translation. Finally, we present initial evaluation results on the utility of collected corpora in domain-adapted machine translation and real-life applications.
Anthology ID:
L12-1554
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
438–445
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/925_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/925_Paper.pdf