Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4973696 | Computer Speech & Language | 2017 | 14 Pages |
Abstract
Lack of parallel corpora have diverted the direction of research towards exploring other arenas to fill in the dearth. Comparable corpora have proved to be a valuable resource in this regard. Interestingly other than the parallel sentences extracted from comparable corpora, parallel phrase fragments have also proved to be beneficial for statistical machine translation. We present a novel approach based on an efficient framework for parallel fragment extraction from comparable corpora. Using the fragments as additional corpus for translation, we are able to obtain an improvement of 0.88 and 0.89 BLEU points on test data for Arabic-English and French-English systems respectively. We have also conducted a detailed analysis of impact of fragments extracted from related vs non-related corpus. A comparison of impact of parallel fragments vs. parallel sentences is also presented highlighting the significance of parallel segments for statistical machine translation. The article concludes with a crude comparative analysis of our approach with an existing fragment extraction technique at various stages of the fragment extraction pipeline.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Sadaf Abdul-Rauf, Holger Schwenk, Mohammad Nawaz,