They represent the sequence as a bag of n-grams, and feed that into the classifier, rather than feeding the sequence directly. The paper basically combines variants on a few old techniques (although a few of the variants are significant and recent), but the interesting result is that they show that put together in the right way and tweaked a little, they're competitive in accuracy with state-of-the-art deep neural network models, at least on some problems, while being much faster to train. Section 2 of the paper, although pretty brief, is where this info is.

Specifically the bag of n-grams can be viewed as a very sparse vector with non-zero entries corresponding to the n-grams in the bag. As a result, n-grams not seen during training need to be ignored.