I don't think you can say that this is a refutation of the Chinese room argument. Here the system doesn't know what the semantics are. For instance, it wouldn't

I don't think you can say that this is a refutation of the Chinese room argument. Here the system doesn't know what the semantics are. For instance, it wouldn't be able to use some kind of logic inference to disambiguate between the two meanings of the same word. It may use some statistical inference (ie. if we talk about meat elsewhere in the sentence, it's likely that Hamburger refers to the sandwich rather than to the inhabitant of Hamburg), but it wouldn't be able to understand the content of the text to provide a translation that derives from this meaning. However, what this article seems to point out is that there is an intermediate representation that is being used within the neural network. This intermediate representation would correspond to a Chinese room where symbols (like hamburger) are first matched to something common to all language (let's call it X), and then translated back to specific languages. What this means is neither the Chinese room argument is true or false, it means that it's more complicated than that. It means that "understanding" has various degrees of depth, and that the first degree of understanding really is just syntactic conceptualization.