The weights are in the Moses config file that is produced by the MERT scripts.
Cheers, Lane On Wed, Sep 21, 2011 at 9:45 AM, Taylor Rose <tr...@languageintelligence.com> wrote: > Thanks for the information Kevin. Where would I find these feature > weights? I've found files in Moses that I suspect might be the weights > but they're not labeled and the file/directory names don't really help > either. > -- > Taylor Rose > Machine Translation Intern > Language Intelligence > IRC: Handle: trose > Server: freenode > > > On Tue, 2011-09-20 at 23:32 -0400, Kevin Gimpel wrote: >> Hey Taylor, >> Sounds like you are trying to come up with a simple heuristic for >> scoring phrase table entries for purposes of pruning. Many choices are >> possible here, so it's good to check the literature as folks mentioned >> above. But as far as I know there's no single optimal answer for this. >> Typically researchers try a few things and use the approach that gives >> the best results on the task at hand. But while there's no single >> correct answer, here are some suggestions: >> If you have trained weights for the features, you should definitely >> use those weights (as Miles suggested). So this would involve >> computing the dot product of the features and weights as follows: >> score(f, e) = \theta_1 * log(p(e | f)) + \theta_2 * log(lex(e | f)) + >> \theta_3 * log(p(f | e)) + \theta_4 * log(lex(f | e)) >> where the thetas are the learned weights for each of the phrase table >> features. >> Note that the phrase table typically stores the feature values as >> probabilities, and Moses takes logs internally before computing the >> dot product. So you should take logs yourself before multiplying by >> the feature weights. >> If you don't have feature weights, using uniform weights is >> reasonable. >> And regarding your original question above: since the phrase penalty >> feature has the same value for all phrase pairs, it shouldn't affect >> pruning, right? >> HTH, >> Kevin >> >> On Tue, Sep 20, 2011 at 4:21 PM, Lane Schwartz <dowob...@gmail.com> >> wrote: >> Taylor, >> >> If you don't have a background in NLP or CL (or even if you >> do), I >> highly recommend taking a look at Philipp's book "Statistical >> Machine >> Translation" >> >> I hope this doesn't come across as RTFM. That's not what I >> mean. :) >> >> Cheers, >> Lane >> >> >> On Tue, Sep 20, 2011 at 3:45 PM, Taylor Rose >> <tr...@languageintelligence.com> wrote: >> > What would happen if I just multiplied the Direct Phrase >> Translation >> > probability φ(e|f) by the Direct Lexical weight Lex(e|f)? >> That seems >> > like it would work? Sorry if I'm asking dumb questions. I >> come from the >> > computational side of computational linguistics. I'm >> learning as fast as >> > I can. >> > -- >> > Taylor Rose >> > Machine Translation Intern >> > Language Intelligence >> > IRC: Handle: trose >> > Server: freenode >> > >> > >> > On Tue, 2011-09-20 at 12:11 -0400, Burger, John D. wrote: >> >> Taylor Rose wrote: >> >> >> >> > So what exactly can I infer from the metrics in the >> phrase table? I want >> >> > to be able to compare phrases to each other. From my >> experience, >> >> > multiplying them and sorting by that number has given me >> more accurate >> >> > phrases... Obviously calling that metric "probability" is >> wrong. My >> >> > question is: What is that metric best indicative of? >> >> >> >> That product has no principled interpretation that I can >> think of. Phrase pairs with high values on all four features >> will obviously have high value products, but that's only >> interesting because all the features happen to be roughly >> monotonic in phrase quality. If you wanted a more principled >> way to rank the phrases, I'd just use the MERT weights for >> those features, and combine them with a dot product. >> >> >> >> Pre-filtering the phrase table is something lots of people >> have looked at, and there are many approaches to this. I like >> this paper: >> >> >> >> Improving Translation Quality by Discarding Most of the >> Phrasetable >> >> Johnson, John Howard; Martin, Joel; Foster, George; Kuhn, >> Roland >> >> >> >> http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=shwart&index=an&req=5763542 >> >> >> >> - JB >> >> >> >> > On Tue, 2011-09-20 at 16:14 +0100, Miles Osborne wrote: >> >> >> exactly, the only correct way to get real probabilities >> out would be >> >> >> to compute the normalising constant and renormalise the >> dot products >> >> >> for each phrase pair. >> >> >> >> >> >> remember that this is best thought of as a set of >> scores, weighted >> >> >> such that the relative proportions of each model are >> balanced >> >> >> >> >> >> Miles >> >> >> >> >> >> On 20 September 2011 16:07, Burger, John D. >> <j...@mitre.org> wrote: >> >> >>> Taylor Rose wrote: >> >> >>> >> >> >>>> I am looking at pruning phrase tables for the >> experiment I'm working on. >> >> >>>> I'm not sure if it would be a good idea to include the >> 'penalty' metric >> >> >>>> when calculating probability. It is my understanding >> that multiplying 4 >> >> >>>> or 5 of the metrics from the phrase table would result >> in a probability >> >> >>>> of the phrase being correct. Is this a good >> understanding or am I >> >> >>>> missing something? >> >> >>> >> >> >>> I don't think this is correct. At runtime all the >> features from the phrase table and a number of other features, >> some only available during decoding, are combined in an inner >> product with a weight vector to score partial translations. I >> believe it's fair to say that at no point is there an explicit >> modeling of "a probability of the phrase being correct", at >> least not in isolation from the partially translated >> sentence. This is not to say you couldn't model this >> yourself, of course. >> >> >>> >> >> >>> - John Burger >> >> >>> MITRE >> >> >>> _______________________________________________ >> >> >>> Moses-support mailing list >> >> >>> Moses-support@mit.edu >> >> >>> http://mailman.mit.edu/mailman/listinfo/moses-support >> >> >>> >> >> >>> >> >> >> >> >> >> >> >> >> >> >> > >> >> > _______________________________________________ >> >> > Moses-support mailing list >> >> > Moses-support@mit.edu >> >> > http://mailman.mit.edu/mailman/listinfo/moses-support >> >> >> >> _______________________________________________ >> >> Moses-support mailing list >> >> Moses-support@mit.edu >> >> http://mailman.mit.edu/mailman/listinfo/moses-support >> > >> > >> > _______________________________________________ >> > Moses-support mailing list >> > Moses-support@mit.edu >> > http://mailman.mit.edu/mailman/listinfo/moses-support >> > >> >> >> >> >> -- >> When a place gets crowded enough to require ID's, social >> collapse is not >> far away. It is time to go elsewhere. The best thing about >> space travel >> is that it made it possible to go elsewhere. >> -- R.A. Heinlein, "Time Enough For Love" >> >> >> _______________________________________________ >> Moses-support mailing list >> Moses-support@mit.edu >> http://mailman.mit.edu/mailman/listinfo/moses-support >> >> > > > _______________________________________________ > Moses-support mailing list > Moses-support@mit.edu > http://mailman.mit.edu/mailman/listinfo/moses-support > -- When a place gets crowded enough to require ID's, social collapse is not far away. It is time to go elsewhere. The best thing about space travel is that it made it possible to go elsewhere. -- R.A. Heinlein, "Time Enough For Love" _______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
