Sure, happy to respond. > Have you worked with professional translators much along the way? Most of us use and keep our own translation memories stretching int

Sure, happy to respond. > Have you worked with professional translators much along the way? Most of us use and keep our own translation memories stretching into the millions of segments. I personally haven't worked too much with translators in my research, but we built OpenNMT with Systran who employ a group of translators and linguists. We also host a yearly workshop (http://workshop-paris-2018.opennmt.net/ http://workshop-paris-2018.opennmt.net/) where many professional translator came to talk. There is a lot of interesting work on integrating translation memories and automatic systems. > What would be the ideal training material for these models to work from? So the curt answer is "the best type of training is more training". In practice though the best type of training is in-domain data for whatever type of problem is currently of interest. > Do you think NMT could ever recognise the type of text to be translated and apply style and context accordingly? Displaying an element of creativity, for example, drawing from relevant contextual themes. I suspect it might do this automatically if trained widely enough? Creativity is a slippery term, so I will avoid that (personally the models don't seem too "clever" to me). There is a lot of interest though in models that can mimic a certain style, whether that be politeness, tone, genre, or technical material. Often that means learning "knobs" to tune for these properties.