Publication - Collecting and Using Comparable Corpora for Statistical Machine Translation

Collecting and Using Comparable Corpora for Statistical Machine Translation

Research Area:  
In Proceedings


Year: 2012
Authors: Inguna Skadiņa; Ahmet Aker; Nikos Mastropavlos; Fangzhong Su; Dan Tufis; Verlic Mateja; Andrejs Vasiļjevs; Bogdan Babych; Paul Clough; Robert Gaizauskas; Nicholas Glaros; Paramita Monica Lestari; Mārcis Pinnis
Book title: On-Line Proceedings of the LREC2012 Conference on Language Resources and Evaluation
Pages: 438-445
Address: Istanbul, Turkey
Organization: ELRA
Date: May 23-25
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.