You could try this tutorial http://www.statmt.org/mtma15/uploads/mtma15-domain-adaptation.pdf
On 14/08/15 20:20, Vincent Nguyen wrote: > I had read this section, which deals with translation model combination. > not much on language model or tuning. > > For instance : if I want to make sure that a specific expression > "titres" is translated in "equities" from French to English. > > These 2 words have specifically to be in the Monolingual corpus of the > language model, or in the parallel corpus ? > > the fact that 2 "parallel expressions" are in the tuning set but not > present in the parallel corpora nor the monolingual LM, can it trigger a > good translation ? > > I am not sure to be clear .... > > thanks again for your help. > > > Le 14/08/2015 20:52, Rico Sennrich a écrit : >> Hi Vincent, >> >> this section describes some domain adaptation methods that are >> implemented in Moses: http://www.statmt.org/moses/?n=Advanced.Domain >> >> It is incomplete (focusing on parallel data and the translation model), >> and does not recommend best practices. >> >> In general, my recommendation is to use in-domain data whenever possible >> (for the language model, translation model, and held-out in-domain data >> for tuning/testing). Out-of-domain data can help, but also hurt your >> system: the effect depends on your domains and the amount of data you >> have for each. Data selection, instance weighting, model interpolation >> and domain features are different methods that give you the benefits of >> out-of-domain data, but reduce its harmful effects, and are often better >> than just concatenating all the data you have. >> >> best wishes, >> Rico >> >> >> On 14/08/15 16:22, Vincent Nguyen wrote: >>> 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 >>> [email protected] >>> http://mailman.mit.edu/mailman/listinfo/moses-support >>> >> _______________________________________________ >> Moses-support mailing list >> [email protected] >> http://mailman.mit.edu/mailman/listinfo/moses-support > _______________________________________________ > Moses-support mailing list > [email protected] > http://mailman.mit.edu/mailman/listinfo/moses-support > -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
