Paper: Joint Language and Translation Modeling with Recurrent Neural Networks

ACL ID D13-1106
Title Joint Language and Translation Modeling with Recurrent Neural Networks
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

We present a joint language and transla- tion model based on a recurrent neural net- work which predicts target words based on an unbounded history of both source and tar- get words. The weaker independence as- sumptions of this model result in a vastly larger search space compared to related feed- forward-based language or translation models. We tackle this issue with a new lattice rescor- ing algorithm and demonstrate its effective- ness empirically. Our joint model builds on a well known recurrent neural network language model (Mikolov, 2012) augmented by a layer of additional inputs from the source language. We show competitive accuracy compared to the traditional channel model features. Our best results improve the output of a system trained on WMT 2012 French-English data by up to 1....