On LM Heuristics for the Cube Growing Algorithm
David Vilar, Hermann Ney · 2009
Current approaches to statistical machine translation try to incorporate more struc-ture into the translation process by includ-ing explicit syntactic information in form of a formal grammar (with a possible, but not necessary, correspondence to a linguis-tic motivated grammar). These more struc-tured models incur into an increased gener-ation cost, and efficient algorithms must be developed. In this paper we concentrate on the cube growing algorithm, a lazy version of the cube grow algorithm. The efficiency of this algorithm depends on a heuristic for language model computation, which is only scarcely discussed in the original pa-per. In this paper we investigate the effect of this heuristic on translation performance and efficiency and propose a new heuris-tic which efficiently decreases memory re-quirements and computation time, while maintaining translation performance. 1