Decoding by Dynamic Chunking for Statistical Machine Translation
Sirvan Yahyaei and Christof Monz
In: MT Summit XII, September 1-4, 2009, Ottawa, Canada.
In this paper we present an extension of a phrase-based decoder that
dynamically chunks, reorders, and applies phrase translations in
tandem. A maximum entropy classifier is trained based on the word
alignments to find the best positions to chunk the source
sentence. No language specific or syntactic information is used to
build the chunking classifier. Words inside the chunks are moved
together to enable the decoder to make long-distance re-orderings to
capture the word order differences between languages with different
sentence structures. To keep the search space manageable, phrases
inside the chunks are monotonically translated, thus by eliminating
the unnecessary local re-orderings, it is possible to perform
long-distance re-orderings beyond the common fixed distortion limit.
Experiments on German to English translation are reported.