Hmm I'm sensing a bit of garbage-in garbage-out here. For starters their original sentences contain unlikely typos instead of homonyms which would be much more

Hmm I'm sensing a bit of garbage-in garbage-out here. For starters their original sentences contain unlikely typos instead of homonyms which would be much more commons. (it complicates the learning as well I'm sure since some changes were made to correct similarly spelled terms which could really change a sentence's meaning once applied) Second, the human corrections aren't that good. We really need to stop creating data sets using anonymous exploited labor that is paid pennies. (they did screen the Amazon Turk users, but if you live in America or work at a university, is there really a shortage of fluent English speakers around you?) Overall I'd say the fluency-editing approach shows promise and would be a boon to ESL-learners, but the training data needs to be improved.