We could counteract this by selecting a higher proportion of examples labelled as 'literal'.
given that the number of literal examples in our data set is relatively small, we would soon deplete our literal instance pool and moreover, because we would be forced to add less confidently labelled examples for the literal class, we are likely to introduce more noise in the training set.