Document-level Re-ranking with Soft Lexical and Semantic Features for Statistical Machine Translation
Chenchen Ding, Masao Utiyama, Eiichiro Sumita · Conference of the Association for Machine Translation in the Americas · 2014
We introduce two document-level features to polish baseline sentence-level translations generated by a state-of-the-art statistical machine translation (SMT) system. One feature uses the word-embedding technique to model the relation between a sentence and its context on the target side; the other feature is a crisp document-level token-type ratio of target-side translations for source-side words to model the lexical consistency in translation. The weights of introduced features are tuned to optimize the sentence- and document-level metrics simultaneously on the basis of Pareto optimality. Experimental results on two different schemes with different corpora illustrate that the proposed approach can efficiently and stably integrate document-level information into a sentence-level SMT system. The best improvements were approximately 0:5 BLEU on test sets with statistical significance.