Are there plans to support Multi-Source Neural Translation (paper: http://www.isi.edu/natural-language/mt/multi-source-neural.pdf and implementation: https://github.com/isi-nlp/Zoph_RNN). The scenario sounds a bit unusually but i think it adapts very well with all the multilingual patterns we already use.
If i get the approach right this (“Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages”) is only a simple solution for what openNMT is already doing when training the models. I think we have to differ the training and the real translation. In the implementation i posted the programm is able to improve the results when it gets the source sentence which should be translated from different languages.
As i mentioned before this scenario isn’t very usally because when you start translating you always have only the source text and none already translated korpus. This would get in the direction of interactive machine translation where you reuse the correction of humans to improve the translation to other languages.
There was discussion of this in our dev channel. It might happen if someone gets interested.
Have you discussed how this fits in the current architecture and which multi-source combination and attention mechanic we want to use?
What we are considering is to introduce a generic multi-encoder approach - which will allow natural parallel sentence analyses (it can be different languages, or some source and pretranslation). It should behave more nicely than just raw concatenation of different sentences.