How to differentiate between real improvement and random noise?

Question

I am building an automatic translator in moses. To improve its performance, I use log-linear weight optimisation. This technique has a random component, which can affect slightly the final result (but I do not know exactly how much).

Suppose that the current performance of the model is 25 BLEU.

Suppose now I modify the language model (e.g. change the smoothing), and I get a performance of 26 BLEU.

My question is: how can I know if the improvement is because the modification, or is just noise from the random component?

Answer 1

This is pretty much what statistics is all about. You can basically do one of the two things (from the basic set of solutions, of course there are many more advanced):

• try to measure/model/quantify the effect of randomness, if you know what is causing it, you might be able to actually compute how much it can affect your model. If analytical solution is not possible, you can always train 20 models with the same data/settings, gather results and estimate noise distribution. Once you have this you can perform statistical tests to check whether the improvement is statistically significant (for example by ANOVA tests).

• simpler approach (but more expensive in terms of data/time) is to simply reduce the variance by averaging. In short - instead of training one model (or evaluating model once) which has this hard to determine noise component - do it many times, 10, 20, and average the results. This way you reduce the variance of the results in your analysis. This can (and should) be combined with the previous option - since now you have 20 results per run, thus you can again use statistical testes to see whether these are significantly different things.