A Generation Framework for Grammar Development

We developed a general framework for the development of a symbolic (hand-written) feature-based lexicalised tree-adjoining grammar (FB-LTAG). We choose natural language generation, surface realisation in particular, to question the capabilities of the grammar in terms of both accuracy and robustness. Our framework combines an optimised surface realiser with efficient error mining techniques. While generating from a large data set provided by the Generation Challenge Surface Realisation task, we improve both accuracy and robustness of our grammar significantly.

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