In Phrase-Based Models of Machine Translation

Hierarchical Models increase the re-ordering capabilities of MT systems by introducing non-terminal symbols to phrases that map source language (SL) words/phrases to the correct position in the target language (TL) translation. Building translations via discontiguous TL phrases increases the difficulty of lan-guage modeling, however, introducing the need for heuristic techniques such as cube pruning (Chiang, 2005), for example. An additional possibility to aid language modeling in hierarchical systems is to use a language model that models fluency of words not using their local context in the string, as in traditional language models, but instead using the deeper context of a word. In this paper, we explore the potential of deep syntax language mod-els providing an interesting comparison with the traditional string-based language model. We include an experimental evalu-ation that compares the two kinds of mod-els independently of any MT system to in-vestigate the possible potential of integrat-ing a deep syntax language model into Hi-erarchical SMT systems