Year: 2007
Type: conference-paper
Abstract: A major engineering challenge in statistical machine translation systems is the efficient representation of extremely large translation rulesets. In phrase-based models, this prob-lem can be addressed by storing the training data in memory and using a suffix array as an efficient index to quickly lookup and ex-tract rules on the fly. Hierarchical phrase-based translation introduces the added wrin-kle of source phrases with gaps. Lookup algorithms used for contiguous phrases no longer apply and the best approximate pat-tern matching algorithms are much too slow, taking several minutes per sentence. We describe new lookup algorithms for hierar-chical phrase-based translation that reduce the empirical computation time by nearly two orders of magnitude, making on-the-fly lookup feasible for source phrases with gaps. 1
Author: Adam Lopez
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2125536435