My dissertation research involved using (and, most importantly, evaluating) GT for certain medical text problems (going from EN->ES), and I found that its performance is highly domain-dependent (i.e., the quality of its translations depends a lot on what kind of input text you're feeding it). Furthermore, I found that whether or not it's "good enough" depends heavily on the user, specifically that user's level of proficiency in English.
GT is better than a lot of machine translation systems, but it still falls victim to a number of common problems that face such systems. Specifically, I've run into a lot of major word-sense-disambiguation issues, especially when working with the sort of short snippets of text that the gem in question uses as examples. Basically, that sort of short bit of text is a worst-case scenario for statistical machine translation, since there's so little context. Google generally does much better with longer runs of text than it does with short phrases.
GT is better than a lot of machine translation systems, but it still falls victim to a number of common problems that face such systems. Specifically, I've run into a lot of major word-sense-disambiguation issues, especially when working with the sort of short snippets of text that the gem in question uses as examples. Basically, that sort of short bit of text is a worst-case scenario for statistical machine translation, since there's so little context. Google generally does much better with longer runs of text than it does with short phrases.