of predicates are based on identifying modal verbs, as well as conditional sentences and modal adverbials. The authors read the modality off parse trees directly using simple structural rules for modifiers.

Earlier work describing the difficulty of correctly translating modality using machine translation includes [@Sigurd:1994] and [@Murata:2005]. Sigurd et al.  write about rule based frameworks and how using alternate grammatical contructions such as the passive can improve the rendering of the modal in the target language. Murata et al.  analyze the translation of Japanese into English by several systems, showing they often render the present incorrectly as the progressive. The authors trained a support vector machine to specifically handle modal contructions, while our modal annotation approach is a part of a full translation system.

We now consider other literature, relating to tree-grafting and machine translation. Our tree-grafting approach builds on a technique used for tree augmentation in Miller et al. , where parse-tree nodes are augmented with semantic categories. In this earlier work, tree nodes were augmented with relations, while we augmented tree nodes with modality and negation. The parser is subsequently retrained for both semantic and syntactic processing. The semantic annotations were done manually by students who were provided 