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 <[email protected]> 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 > <[email protected]> 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. <[email protected]> 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 > >> >>> [email protected] > >> >>> http://mailman.mit.edu/mailman/listinfo/moses-support > >> >>> > >> >>> > >> >> > >> >> > >> >> > >> > > >> > _______________________________________________ > >> > Moses-support mailing list > >> > [email protected] > >> > http://mailman.mit.edu/mailman/listinfo/moses-support > >> > >> _______________________________________________ > >> Moses-support mailing list > >> [email protected] > >> http://mailman.mit.edu/mailman/listinfo/moses-support > > > > > > _______________________________________________ > > Moses-support mailing list > > [email protected] > > 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 > [email protected] > http://mailman.mit.edu/mailman/listinfo/moses-support >
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