I have the same question about Google translate. I'm not convinced that its errors can simply be chalked up to neural networks. As you said, they ought to be constantly trying to improve their word segmentation and datasets.
I'm not convinced that Google Translate is as major of a focus at Google as other things.
Perhaps I'm wrong, and it's just a hard problem, but the translations I've seen haven't improved as much over the years as I expected given progress in other areas of AI.
They may be more focused on adding new languages than improving existing ones. Don't know.
Japanese translation is pretty good. Chinese is bad. I guess Chinese is harder because nobody really uses any phonetic writing for reading beyond grade schools. And then there is traditional/simplified, and, I imagine, other differences between how different regions use characters. Baidu's translate is better than Facebook/Google in some cases.
I wonder if translation just isn't as sexy as image recognition and self driving cars, therefore research dollars aren't as focused on it.
Machine translation has a lot of research funding, and has had it for decades.
I'm not sure about USA, but it's been a major focus of very large EU grant programs due to the obvious multilinguality of EU and the explicit goal to move towards a single European market by reducing barriers in trade, including language barrier.
It also has many commercial use cases and thus has always had quite a lot of people and teams working on it compared to other fields of ML or NLP.
The problem is that it's hard. Every 0.1% of progress has historically required a lot of work.
Now that the general public understands a bit more about NN's , AI and machine learning, maybe there will be a renewed push to invest in even better machine translation.
More research into theory, more / better data from professional translators, and more efficient implementations from engineers.
I'm not convinced that Google Translate is as major of a focus at Google as other things.
Perhaps I'm wrong, and it's just a hard problem, but the translations I've seen haven't improved as much over the years as I expected given progress in other areas of AI.
They may be more focused on adding new languages than improving existing ones. Don't know.
Japanese translation is pretty good. Chinese is bad. I guess Chinese is harder because nobody really uses any phonetic writing for reading beyond grade schools. And then there is traditional/simplified, and, I imagine, other differences between how different regions use characters. Baidu's translate is better than Facebook/Google in some cases.
I wonder if translation just isn't as sexy as image recognition and self driving cars, therefore research dollars aren't as focused on it.