Key points are not available for this paper at this time.
In this paper we describe a novel distributed language model for N-best list re-ranking. The model is based on the client/server paradigm where each server hosts a portion of the data and provides information to the client. This model allows for using an arbitrarily large corpus in a very efficient way. It also provides a natural platform for relevance weighting and selection. We applied this model on a 2.97 billion-word corpus and re-ranked the N-best list from Hiero, a state-of-the-art phrase-based system. Using BLEU as a metric, the re-ranked translation achieves a relative improvement of 4.8%, significantly better than the model-best translation.
Zhang et al. (Sun,) studied this question.