Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Computational Linguistics
Extracting parallel sub-sentential fragments from non-parallel corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
On the use of comparable corpora to improve SMT performance
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Statistical Machine Translation
Statistical Machine Translation
Improving SMT for Baltic Languages with Factored Models
Proceedings of the 2010 conference on Human Language Technologies -- The Baltic Perspective: Proceedings of the Fourth International Conference Baltic HLT 2010
Parallel sentence generation from comparable corpora for improved SMT
Machine Translation
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
LetsMT!: a cloud-based platform for do-it-yourself machine translation
ACL '12 Proceedings of the ACL 2012 System Demonstrations
ACCURAT toolkit for multi-level alignment and information extraction from comparable corpora
ACL '12 Proceedings of the ACL 2012 System Demonstrations
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In the recent years, statistical machine translation (SMT) has received much attention from language technology researchers and it is more and more applied not only to widely used language pairs, but also to under-resourced languages. However, under-resourced languages and narrow domains face the problem of insufficient parallel data for building SMT systems of reasonable quality for practical applications. In this paper we show how broad domain SMT systems can be successfully tailored to narrow domains using data extracted from strongly comparable corpora. We describe our experiments on adaptation of a baseline English-Latvian SMT system trained on publicly available parallel data (mostly legal texts) to the information technology domain by adding data extracted from in-domain comparable corpora. In addition to comparative human evaluation the adapted SMT system was also evaluated in a real life localisation scenario. Application of comparable corpora provides significant improvements increasing human translation productivity by 13.6% while maintaining an acceptable quality of translation.