Hi, obviously word alignment is very important for extraction of the most useful phrases. What about other ways for phrase extraction?
In your text book 'Statistical Machine Translation' you do mention an other approach: "we may also use the expectation maximization algorithm to directly find phrase alignments for sentence pairs." (p. 8) Any work on that? Any success? Pros and cons? Further in the paper "Philipp Koehn, Franz Josef Och, Daniel Marcu: Statistical Phrase-Based Translation (2003)" I've found the surprising conclusion: "straight-forward syntax-based mappings do not lead to better translations than unmotivated phrase mappings." Has anyone done any more research on this? How do this compare to tree-based models and factored translations models, respectively? Yours, Per Tunedal On Wed, Apr 3, 2013, at 17:13, Philipp Koehn wrote: > Hi, > > > > As a bonus, the fig. 5 explains how the > > phrases are extracted: "consistent word alignments". Finally I > > understand :-) > > > > It might be a good idea to include that illustration on the page: > > http://www.statmt.org/moses/?n=Moses.Background > > It would make the concept easy to understand. > > Are you saying that > > BP(f1J,e1J,A) = { ( fjj+m,eii+n ) }: forall (i',j') in A : j <= j' <= > j+m <-> i <= i' <= i+n > > was not clear enough? > > Okay, I added the picture... > > -phi > -- snip-- _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
