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
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