Decision Trees for Lexical Smoothing in Statistical Machine Translation

Context Aware Smoothing for Neural Machine Translation Proceedings of the The 8th International Joint Conference on Natural Language Processing, pages 11?20, Taipei, Taiwan, November 27

Meta level Statistical Machine Translation International Joint Conference on Natural Language Processing, pages 1151?1157, Nagoya, Japan, 14 18 October 2013 Meta level Statistical

In this paper, we investigate three issues of term translation in the context of document- informed SMT and propose three cor- responding models: (a) a term trans- lation

Although the focus of research in machine transla- tion has firmly moved onto neural machine trans- lation, we still built traditional phrase-based statis- tical machine

In order to provide points of comparison with the 2009 Workshop on Statistical Machine Trans- lation shared translation task participants, table 4 lists automatic metric scores for

Statistical machine translation facilitates us to automatically build machine translation systems using statistical models capable by text data. The statistical models comprise

For instance, in statistical machine trans- lation (SMT), [12, 13] showed how the output of a trans- lation model can be used iteratively to improve results in a task like

Sharing source and target side vocabularies and word embed- dings has been a popular practice in neural machine trans- lation (briefly, NMT) for similar languages (e.g., English