Feels like a lot of the counter examples listed involve contractions and conjugation errors. Saying 'like' and 'liked' are different words is a strong interpretation. Similarly, 'I am' and 'I'm' are really not distinct words so counting that toward an error rate is a bit too literal. The objections could be solved by a decent parser. That said, weighting insertions and deletions equally is clearly a problem. Certain words ought to have more weight in a model. Weighting words by something like 1/log(frequency) might be a good start since less common words tend to be more important for meaning.