Reranking Translation Hypotheses Using Structural Properties

We investigate methods that add syntactically motivated features to a statistical machine translation system in a reranking framework. The goal is to analyze whether shallow parsing techniques help in identifying ungrammatical hypotheses. We show that improvements are possible by utilizing supertagging, lightweight dependency analysis, a link grammar parser and a maximum-entropy based chunk parser. Adding features to n-best lists and discriminatively training the system on a development set increases the BLEU score up to 0.7% on the test set.