Miles Osborne a écrit : > 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).
What part of MERT is not deterministic ? Hopefully, there is a way to make it so (by explicitly initialising the random seed to some known value, for example). I would feel safer with a fully deterministic procedure :-] (the first run may be "unpredictable", but any re-run provides the exact same result). In my experience, this is already the case. Could it be system-specific, then? Thanks, -- Daniel > 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. _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
