Chris is correct --MERT has no guarantees that it will produce the same results between runs (even when starting from the same training conditions). This is in part because MERT does not find the global optimum (remember it is not considering the full space of possible translations, but rather uses n-best lists).
However, you can reuse weights between runs for development experiments if you are just changing a single feature function. You may not get the best possible results, but your experiments should be in the right area. Naturally, you will eventually need to re-run MERT to 'sync' your model. Miles On 21/01/2008, Daniel Déchelotte <[EMAIL PROTECTED]> wrote: > > Chris Dyer a écrit : > > > menor bangget a écrit : > > > > > 2. If I train the same corpus twice, using 2 different word > > > alignment, e.g., union and grow-diag-final, will I get different > > > weight after running mert-moses.pl; or it will be the same because > > > I used exactly the same corpus? > > > > MERT is a non-deterministic algorithm and so you'll see different > > weights from run to run, even with the exact same alignment > > heuristics. > > AFAIK, mert picks some points at random indeed, but it picks the exact > same points from run to run (on the same data). In other words, > rerunning it on the same models (same data + same training sequence) > will provide the same results. > > > But, yes, in general, changing any aspect of the system, > > including the alignment heuristic used, will result in different > > weights. > > Ack. > > -- Daniel > _______________________________________________ > Moses-support mailing list > [email protected] > http://mailman.mit.edu/mailman/listinfo/moses-support > >
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