Heaps of split-states 4:  for all t in T do Find best final split-state 23: end for 25: return I.MAX() 26: end function Figure 2: Push-forward rescoring with a recurrent neural network language model given a beam-width for language model split-states k, decoder states V , edges E, a start state s and final states T .
a recurrent neural network language model makes much weaker independence assumptions.