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 <[email protected]> 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 > > > > _______________________________________________ > Moses-support mailing > [email protected]http://mailman.mit.edu/mailman/listinfo/moses-support > > > > _______________________________________________ > Moses-support mailing list > [email protected] > http://mailman.mit.edu/mailman/listinfo/moses-support > > -- Raj Dabre. Doctoral Student, Graduate School of Informatics, Kyoto University. CSE MTech, IITB., 2011-2014
_______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
