Using Dependency Grammars in guiding templatic Natural Language Generation

We propose a templatic Natural Language Generation system, which uses a dependency grammar together with feature structure unification to guide the generation process. Feature structures are unified across dependency arcs, licensing the selection of correct lexical forms. From a practical perspective, the system has numerous advantages, such as the possibility to easily mix static and dynamic content. From a theoretical point of view, the templates can be seen as linguistic constructions, of which the relevant grammar is specified in terms of dependency grammar. In this paper we present the architecture of the system, and two case studies: verbal agreement in French, including the object-agreement pattern of past participles, and definiteness spreading in Scandinavian languages. The latter case study also exemplifies how this framework can be used for cross-lingual comparison and generation.

• Machine Intelligence

• Natural Language Processing