Mixture modelling is a standard technique for density estimation, but its use in statistical machine translation (SMT) has just started to be explored. One of the main advantages of this technique is its capability to learn specific probability distributions that better fit subsets of the training dataset. This feature is even more important in SMT given the difficulties to translate polysemic terms whose semantic depends on the context in which that term appears. In this paper, we describe a mixture extension of the HMM alignment model and the derivation of Viterbi alignments to feed a state-of-the-art phrase-based system. Experiments carried out on the Europarl and News Commentary corpora show the potential interest and limitations of mixture modelling.
REF proposed a model that can be used to generate topic-dependent alignments by extension of the HMM alignment model and derivation of Viterbi alignments.
Domain Adaptation in Statistical Machine Translation with Mixture Modelling
Workshop on Statistical Machine Translation
Computer Science