...and I wouldn't be surprised to find Moses also behind this Java-to-C# automatic translation:
https://www.youtube.com/watch?v=CHDDNnRm-g8 O. ----- Original Message ----- > From: "Marcin Junczys-Dowmunt" <junc...@amu.edu.pl> > To: moses-support@mit.edu > Sent: Friday, 19 June, 2015 19:21:45 > Subject: Re: [Moses-support] Major bug found in Moses > On that interesting idea that moses should be naturally good at > translating things, just for general considerations. > > Since some said this thread has educational value I would like to share > something that might not be obvious due to the SMT-biased posts here. > Moses is also the _leading_ tool for automatic grammatical error > correction (GEC) right now. The first and third system of the CoNLL > shared task 2014 were based on Moses. By now I have results that surpass > the CoNLL results by far by adding some specialized features to Moses > (which thanks to Hieu is very easy). > > It even gets good results for GEC when you do crazy things like > inverting the TM (so it should actually make the input worse) provided > you tune on the correct metric and for the correct task. The interaction > of all the other features after tuning makes that possible. > > So, if anything, Moses is just a very flexible text-rewriting tool. > Tuning (and data) turns into a translator, GEC tool, POS-tagger, > Chunker, Semantic Tagger etc. > > On 19.06.2015 18:40, Lane Schwartz wrote: >> On Fri, Jun 19, 2015 at 11:28 AM, Read, James C <jcr...@essex.ac.uk >> <mailto:jcr...@essex.ac.uk>> wrote: >> >> What I take issue with is the en-masse denial that there is a >> problem with the system if it behaves in such a way with no LM + >> no pruning and/or tuning. >> >> >> There is no mass denial taking place. >> >> Regardless of whether or not you tune, the decoder will do its best to >> find translations with the highest model score. That is the expected >> behavior. >> >> What I have tried to tell you, and what other people have tried to >> tell you, is that translations with high model scores are not >> necessarily good translations. >> >> We all want our models to be such that high model scores correspond to >> good translations, and that low model scores correspond with bad >> translations. But unfortunately, our models do not innately have this >> characteristic. We all know this. We also know a good way to deal with >> this shortcoming, namely tuning. Tuning is the process by which we >> attempt to ensure that high model scores correspond to high quality >> translations, and that low model scores correspond to low quality >> translations. >> >> If you can design models that naturally correspond with translation >> quality without tuning, that's great. If you can do that, you've got a >> great shot at winning a Best Paper award at ACL. >> >> In the meantime, you may want to consider an apology for your rude >> behavior and unprofessional attitude. >> >> Goodbye. >> Lane >> >> >> >> _______________________________________________ >> Moses-support mailing list >> Moses-support@mit.edu >> http://mailman.mit.edu/mailman/listinfo/moses-support > > _______________________________________________ > Moses-support mailing list > Moses-support@mit.edu > http://mailman.mit.edu/mailman/listinfo/moses-support -- Ondrej Bojar (mailto:o...@cuni.cz / bo...@ufal.mff.cuni.cz) http://www.cuni.cz/~obo _______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
