Alternatively, any sequence model can be turned into a bidirectional model by using multiple passes. For example, the first pass would use only part-of-speech features from already-disambiguated words on the left. In the second pass, tags for all words, including those on the right, can be used. Alternately, the tagger can be run twice, once left-to-right and once right-to-left. In Viterbi decoding, the labeler would chooses the higher scoring of the two sequences (left-to-right or right-to-left). Bidirectional models are quite standard for neural models, as we will see with the biLSTM models to be introduced in Chapter 9.