Accuracy is what matters if you're trying to build something that translates. You can judge if it's good or not based on (subjective or objective) measures of a

Accuracy is what matters if you're trying to build something that translates. You can judge if it's good or not based on (subjective or objective) measures of accuracy. But if you're at 75% per cent and want to get to 100%, you need to understand what the problem is with the rest. And if it's 75% of "perfect translation of single written sentences from newspapers or technical litterature", how far along is that towards something "being part of an everyday conversation"? I studied linguistics at a university where focus was very much spoken language, sociolinguistics, language in context, before moving into (or through) NLP, and the distance between what a statistical machine translation system is able to handle and the stuff I used to work with is very large. A lot of NLP work now seems to focus on algorithms, but intuitively it seems to me that a much larger issue is the quality of the data, in the sense that humans don't learn language from piles of isolated text and somehow we're expecting machines to do it.. Rext is a lossy encoding of spoken language, even if you try your best to mimic it, but more seriously it does not include the physical context that children encounter language in. The learning situations aren't the same, I don't know why we're expecting the results to be.