Try using -bayes 0 when running the interpolated model.
Without it, ngram will construct a merged ngram model in memory,
which does not work well when combining word and class-based models.
--Andreas
In message <1109146260.28101.1.camel@markov>you wrote:
> Hello,
>
> I'm a bit confused with interpolation.
> I want to calculate test text's perplexity using different interpolation
> weights (lambdas). Everything is OK until I set lambda to 1.0. Shouldn't
> I then get the same perplexity as using only the base language model?
> This doesn't seem to be the case:
>
> $ ngram -lm trigram.arpa -ppl <testtxt>
> file <testtxt>: 2394 sentences, 29475 words, 1224 OOVs
> 0 zeroprobs, logprob= -86274.9 ppl= 653.583 ppl1= 1132.06
>
> $ ngram -lm trigram.arpa -ppl <testtxt> -classes <classdefs> -mix-lm
> class-trigram.arpa -lambda 1.0
> file <testtxt>: 2394 sentences, 29475 words, 1224 OOVs
> 0 zeroprobs, logprob= -85554.4 ppl= 619.144 ppl1= 1067.5
>
> As shown, the perplexity is 653.539 when using standalone trigram, and
> 619.144 when interpolating the trigram with the class-trigam, using
> lambda 1.0. Why are they not equal?
>
> Both word trigram and class trigram are close-vocabulary LMs, if it
> matters.
>
> Regards,
>
> Tanel A.
>
>
>
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