Barry Haddow wrote:

> By default, mert employs random restarts in its search. These can be  
> turned
> off using -predictable-seeds. Are you using random restarts?

Even with this off we've seen occasional non-determinism.

- JB

> On Thursday 24 March 2011 15:07, John Burger wrote:
>> Lane Schwartz wrote:
>>> I've examined the n-best lists, and it seems there are at least a
>>> couple of interesting cases. In the simplest case, several
>>> translations of a given sentence produce the exact same score, and
>>> these tied translations appear in different order during different
>>> runs. This is a bit odd, but [not] terribly worrisome. The stranger
>>> case is when there are two different decoding runs, and for a given
>>> sentence, there are translations that appear only in run A, and
>>> different translations that only appear in run B.
>>
>> Both these cases are relevant to something we've occasionally seen,
>> which is non-determinism during =tuning=.  This is not surprising
>> given the above, since tuning of course involves decoding.  It's hard
>> to reproduce, but we have sometimes seen very different weights  
>> coming
>> out of MERT for the exact same system configurations.  The problem
>> here is that even very small differences in tuning can result in
>> substantial differences in test results, because of how twitchy  
>> BLEU is.
>>
>> Like many folks, we typically run MERT on a cluster.  This brings up
>> another source of non-determinism we've theorized about.  Some of our
>> clusters are heterogenous, and we've wondered if there might be minor
>> differences in floating point behavior from machine to machine.  The
>> assignment of different chunks of the tuning data to different
>> machines is typically non-deterministic, so this might carry over to
>> the actual weights that come out of MERT.
>>
>> Does anyone know how robust the floating point usage in the decoder  
>> is
>> under these circumstances?
>>
>> Thanks.
>>
>> - John Burger
>>   MITRE
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