Computational Linguistics - Special issue on web as corpus
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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
A simple sentence-level extraction algorithm for comparable data
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Extracting parallel sentences from comparable corpora using document level alignment
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
TransSearch: from a bilingual concordancer to a translation finder
Machine Translation
Crisis MT: developing a cookbook for MT in crisis situations
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Transliteration mining using large training and test sets
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Twitter translation using translation-based cross-lingual retrieval
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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Mining parallel data from comparable corpora is a promising approach for overcoming the data sparseness in statistical machine translation and other NLP applications. Even if two comparable documents have few or no parallel sentence pairs, there is still potential for parallelism in the sub-sentential level. The ability to detect these phrases creates a valuable resource, especially for low-resource languages. In this paper we explore three phrase alignment approaches to detect parallel phrase pairs embedded in comparable sentences: the standard phrase extraction algorithm, which relies on the Viterbi path; a phrase extraction approach that does not rely on the Viterbi path, but uses only lexical features; and a binary classifier that detects parallel phrase pairs when presented with a large collection of phrase pair candidates. We evaluate the effectiveness of these approaches in detecting alignments for phrase pairs that have a known alignment in comparable sentence pairs. The results show that the Non-Viterbi alignment approach outperforms the other two approaches on F1 measure.