Hi Raj,
the information you provide is pretty vague, so I'm just making some
wild guesses here:
it could be a user error, for instance an inconsistency between the
training sets used for training BilingualNPLM and the phrase table.
Check that the same version of the corpus (including tokenization,
truecasing etc.) was used for training, and that you did not mix up
source and target language. Also check that the settings during training
are consistent with those in the moses.ini file.
it's possible that some of the settings (vocabulary size, number of
training epochs, or similar) are unsuitable for your task. For example,
since you have a relatively small training corpus, you may need more
epochs of training to get good results (use a validation set to see if
model perplexity converges).
please double-check that there were no problems with the
unicode-handling of Japanese/Chinese characters, and that the encoding
of your vocabulary files matches that of the translation model, and the
decoder input. We have never experienced such problems, but they could
arise for some system configurations.
best wishes,
Rico
On 06.07.2015 16:31, Raj Dabre wrote:
Hello Rico,
I trained both mono as well as bilingual LM's.
Both seemed ineffective.
As I mentioned before, I am working with Chinese-Japanese and the
domain is paper abstracts.
I did check the n-best lists and I saw a significant difference
between the LM scores when comparing the runs for KenLm and NPLM.
What could have gone wrong during the training?
Regards.
On Mon, Jul 6, 2015 at 10:53 PM, Rico Sennrich <rico.sennr...@gmx.ch
<mailto:rico.sennr...@gmx.ch>> wrote:
Hello Raj,
can you please clarify if you tried to train a monolingual LM
(NeuralLM), a bilingual LM (BilingualNPLM), or both? Our previous
experiences with BilingualNPLM are mixed, and we observed
improvements for some tasks and language pairs, but not for
others. See for instance:
Alexandra Birch, Matthias Huck, Nadir Durrani, Nikolay Bogoychev
and Philipp Koehn. 2014. Edinburgh SLT and MT System Description
for the IWSLT 2014 Evaluation. Proceedings of IWSLT 2014.
To help debugging, you can check the scores in the n-best lists of
the tuning runs. If the NPLM features give much higher costs than
KenLM (trained on the same data), this can indicate that something
went wrong during training.
best wishes,
Rico
On 06.07.2015 14:29, Raj Dabre wrote:
Dear all,
I have checked out the latest version of moses and nplm and
compiled moses successfully with the --with-nplm option.
I got a ton of warnings during compilation but in the end it all
worked out and all the desired binaries were created. Simply
executing the moses binary told me the the BilingualNPLM and
NeuralLM features were available.
I trained an NPLM model based on the instructions here:
http://www.statmt.org/moses/?n=FactoredTraining.BuildingLanguageModel#ntoc33
The corpus size I used was about 600k lines (for
Chinese-Japanese; Target is Japanese)
I then integrated the resultant language model (after 10
iterations) into the decoding process by moses.ini
I initiated tuning (standard parameters) and I got no errors,
which means that the neural language model (NPLM) was recognized
and queried appropriately.
I also ran tuning without a language model.
The strange thing is that the tuning and test BLEU scores for
both these cases are almost the same. I checked the weights and
saw that the LM was assigned a very low weight.
On the other hand when I used KENLM on the same data.... I had
comparatively higher BLEU scores.
Am I missing something? Am I using the NeuralLM in an incorrect way?
Thanks in advance.
--
Raj Dabre.
Doctoral Student,
Graduate School of Informatics,
Kyoto University.
CSE MTech, IITB., 2011-2014
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--
Raj Dabre.
Doctoral Student,
Graduate School of Informatics,
Kyoto University.
CSE MTech, IITB., 2011-2014
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