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
