Mining Parallel Resources for Machine Translation from Comparable Corpora

Santanu Pal; Partha Pakray; Alexander Gelbukh; Josef van Genabith

In: Alexander Gelbukh (Hrsg.). Computational Linguistics and Intelligent Text Processing, 16th International Conference, CICLing 2015, Proceedings. International Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2015), April 14-20, Cairo, Egypt, Pages 534-544, Lecture Notes in Computer Science (LNCS), Vol. 9041, ISBN 978-3-319-18110-3, Springer, 2015.


Good performance of Statistical Machine Translation (SMT) is usually achieved with huge parallel bilingual training corpora, because the translations of words or phrases are computed basing on bilingual data. However, in case of low-resource language pairs such as English-Bengali, the performance is affected by insufficient amount of bilingual training data. Recently, comparable corpora became widely considered as valuable resources for machine translation. Though very few cases of sub-sentential level parallelism are found between two comparable documents, there are still potential parallel phrases in comparable corpora. Mining parallel data from comparable corpora is a promising approach to collect more parallel training data for SMT. In this paper, we propose an automatic alignment of English- Bengali comparable sentences from comparable documents. We use a novel textual entailment method and distributional semantics for text similarity. Subsequently, we apply template-based phrase extraction technique to aligned parallel phrases from comparable sentence pairs. The effectiveness of our approach is demonstrated by using parallel phrases as additional training examples for an English-Bengali phrase-based SMT system. Our system achieves significant improvement in terms of translation quality over the baseline system.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence