Undirected Machine Translation with Discriminative Reinforcement Learning
Andréa Gesmundo, James Henderson · 2014
We present a novel Undirected Machine Translation model of Hierarchical MT that is not constrained to the standard bottomup inference order.Removing the ordering constraint makes it possible to condition on top-down structure and surrounding context.This allows the introduction of a new class of contextual features that are not constrained to condition only on the bottom-up context.The model builds translation-derivations efficiently in a greedy fashion.It is trained to learn to choose jointly the best action and the best inference order.Experiments show that the decoding time is halved and forestrescoring is 6 times faster, while reaching accuracy not significantly different from state of the art.