A syntactic skeleton for statistical machine translation
Bart Mellebeek, Karolina Owczarzak, Declan Groves, Josef van Genabith, Andy Way · Arrow@dit (Dublin Institute of Technology) · 2006
We present a method for improving statistical machine translation perfor-mance by using linguistically motivated syntactic information. Our algo-rithm recursively decomposes source language sentences into syntactically simpler and shorter chunks, and recomposes their translation to form target language sentences. This improves both the word order and lexical selection of the translation. We report statistically signicant relative improvements of 3.3 % BLEU score in an experiment (English!Spanish) carried out on an 800-sentence test set extracted from the Europarl corpus. 1