text: We describe a unified and coherent syntactic framework for supporting a semanticallyinformed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality—and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.
  @cite_0: Since 1995, a few statistical parsing algorithms have demonstrated a breakthrough in parsing accuracy, as measured against the UPenn TREEBANK as a gold standard. In this paper we report adapting a lexicalized, probabilistic context-free parser to information extraction and evaluate this new technique on MUC-7 template elements and template relations.
summary: Our tree-grafting approach is related to a technique used for tree augmentation in @cite_0 , where parse-tree nodes are augmented with semantic categories. @ augment tree nodes with named entities and relations, while we used named entities and modalities. The parser is subsequently retrained for both semantic and syntactic processing. The semantic annotations were done manually by students following a set of guidelines and then merged with the syntactic trees automatically. In our work we tagged our corpus with entities and modalities automatically and then grafted them onto the syntactic trees automatically, for the purpose of training a statistical machine translation system. An added benefit of the extracted translation rules is that they are capable of producing semantically-tagged Urdu parses, despite that the training data were processed by only an English parser and tagger.