As maximizing F (θ, q) involves minimizing the KL divergence, Ganchev et al.
2010present a minorization-maximization algorithm akin to EM at sentence level: directly applying posterior regularization to neural machine translation faces a major difficulty: it is hard to specify the hyper-parameter b to effectively bound the expectation of features, which are usually real-valued in translation (Och and Ney, 2002;Koehn et al., 2003;Chiang, 2005).