That would make very cool student projects. Also that video is acing it, even the voice-over is synthetic :)
On 23.06.2015 00:27, Ondrej Bojar wrote: > ...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 _______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
