Minimum Error Rate Training Semiring
Artem Sokolov, François Yvon, Limsi-Cnrs LIMSI-CNRs, Paris Sud · 2011
Modern Statistical Machine Translation (SMT) systems make their decisions based on multiple information sources, which as-sess various aspects of the match between a source sentence and its possible trans-lation(s). Tuning a SMT system consists in finding the right balance between these sources so as to produce the best possi-ble output, and is usually achieved through Minimum Error Rate Training (MERT) (Och, 2003). In this paper, we recast the operations implied in MERT in the terms of operations over a specific semir-ing, which, in particular, enables us to de-rive a simple and generic implementation of MERT over word lattices. 1