Fast Translation Rule Matching for Syntax-based Statistical Machine Translation

In a linguistically-motivated syntax-based translation system, the entire translation process is normally carried out in two steps, translation rule matching and target sentence decoding using the matched rules. Both steps are very timeconsuming due to the tremendous number of translation rules, the exhaustive search in translation rule matching and the complex nature of the translation task itself. In this paper, we propose a hyper-tree-based fast algorithm for translation rule matching. Experimental results on the NIST MT-2003 Chinese-English translation task show that our algorithm is at least 19 times faster in rule matching and is able to help to save 57% of overall translation time over previous methods when using large fragment translation rules.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Hui Zhang, Min Zhang, Haizhou Li, Chew Lim Tan
• Keywords: EMNLP 2009, Linguistically-motivated Syntax-based Translation, Natural Language Processing, Translation Rules, Translation Task