Hi, I can't find a sort of "tutorial " on domain adaptation path to follow. I read this in the doc : The language model should be trained on a corpus that is suitable to the domain. If the translation model is trained on a parallel corpus, then the language model should be trained on the output side of that corpus, although using additional training data is often beneficial.
And in the training section of the EMS, there is a sub section with domain-features=.... What is the best practice ? Let's say for instance that I would like to specialize my modem in finance translation, with specific corpus. Should I train the Language model with finance stuff ? Should I include parallel corpus in the translation model training ? Should I tune with financial data sets ? Please help me to understand. Vincent _______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
