Investigations in Exact Inference for Hierarchical Translation
Wilker Ferreira Aziz, Marc Dymetman, Sriram Venkatapathy · 2013
We present a method for inference in hierarchical phrase-based translation, where both optimisation and sampling are performed in a common exact inference framework related to adaptive rejection sampling. We also present a first implementation of that method along with experimental results shedding light on some fundamental issues. In hierarchical translation, inference needs to be performed over a high-complexity distribution defined by the intersection of a translation hypergraph and a target language model. We replace this intractable distribution by a sequence of tractable upper-bounds for which exact optimisers and samplers are easy to obtain. Our experiments show that exact inference is then feasible using only a fraction of the time and space that would be required by the full intersection, without recourse to pruning techniques that only provide approximate solutions. While the current implementation is limited in the size of inputs it can handle in reasonable time, our experiments provide insights towards obtaining future speedups, while staying in the same general framework. 1