This paper presents the Bremen system for the GIVE-2.5 challenge. It is based on decision trees learnt from new annotations of the GIVE corpus augmented with manually specified rules. Surface realisation is based on context-free grammars. The paper will address advantages and shortcomings of the approach and discuss how the present system can serve as a baseline for a future evaluation with an improved version using hierarchical reinforcement learning with graphical models. 