Using Dependency Grammars in guiding Natural Language Generation

Anton Ivanov
The Israeli Seminar of Computational Linguistics, IBM Research, Haifa(2019)


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 allows for numerous advantages, as the possibility easily to 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.