John, this is true if there were three tokens, but {}Processor{} has no
spaces. Assuming that the target language should be {}processeur{}
without spaces in both the parallel and LM data, the tables and the
language model will treat it as one token and not break break it up.
Henry, I suspect your corpus preparation inserts spaces between to
create {} Processor {} (3 tokens). John's description is much more
viable if this is the case.
One oddity is the output {}{} token because it's one token, not two.
Moses won't remove the space to splice the two. It would seem your
target data contains this as a token from somewhere in the tables or LM.
I suggest you double-check your tokenization and other preparation to
ensure source and target are still one token when you start training.
Tom
On Tue, 31 Jul 2012 10:08:43 -0400, John D Burger <[email protected]>
wrote:
> Are there any such placeholders in your language modeling data and
> your parallel training data? If not, all the models are going to
> treat them as unknown words. In the case of the language model, it
> doesn't surprise me too much that the placeholders all get pushed
> together, as that will produce fewer discontiguous subsequences,
> which
> the language model will prefer.
>
> - John Burger
> MITRE
>
> On Jul 31, 2012, at 03:05 , Henry Hu wrote:
>
>> Hi,
>>
>> I use a model to translate English to French. First, I replaced HTML
>> tags such as <a>, <b>, with the placeholder {}, like this:
>>
>> {}Processor{}
>>
>> Then decoding. To my confusion, I got the result:
>>
>> {}{} processeur
>>
>> instead of {}processeur{}. Why did the placeholder move? How can I
>> make it fixed? Thanks for any suggestion.
>>
>> Henry
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