Paper: Undirected Machine Translation with Discriminative Reinforcement Learning

ACL ID E14-1002
Title Undirected Machine Translation with Discriminative Reinforcement Learning
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We present a novel Undirected Machine Translation model of Hierarchical MT that is not constrained to the standard bottom- up inference order. Removing the order- ing constraint makes it possible to condi- tion on top-down structure and surround- ing context. This allows the introduc- tion 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 forest- rescoring is 6 times faster, while reaching accuracy not significantly different from state of the art.