Paper: Scalable Decipherment for Machine Translation via Hash Sampling

ACL ID P13-1036
Title Scalable Decipherment for Machine Translation via Hash Sampling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

In this paper, we propose a new Bayesian inference method to train statistical ma- chine translation systems using only non- parallel corpora. Following a probabilis- tic decipherment approach, we first intro- duce a new framework for decipherment training that is flexible enough to incorpo- rate any number/type of features (besides simple bag-of-words) as side-information used for estimating translation models. In order to perform fast, efficient Bayesian inference in this framework, we then de- rive a hash sampling strategy that is in- spired by the work of Ahmed et al. (2012). The new translation hash sampler enables us to scale elegantly to complex mod- els (for the first time) and large vocab- ulary/corpora sizes. We show empirical results on the OPUS data?our method yields the best B...