In a spoken dialogue system, semantic interpretations are based on relations with words and phrases. Automatic learning of these relations is discussed and a solution based on semantic classification trees is presented. Interpretations can be complete if they provide the dialogue component with all the required information needed to produce an answer to the user. They can also be incomplete or there may be more than one complete or incomplete interpretations in competition, due to inherent ambiguities of the spoken message or to imprecision of the recognizer. Solutions for semantic completion and disambiguation will make dialogue systems more powerful and flexible. Preliminary results and future perspectives are discussed. Conceptual and language models (LM) can be dynamically adapted taking the dialogue history into account. Histories can be clustered into ``dialogue situations'' by considering the predicates used in the logic formula for generating an output message and selecting the one that is more likely to condition the answer. A dynamic probability can be expressed as combination of LM probabilities in various situations. Each probability is weighted according to the dialogue situation. Better LMs conceived with these principles will make future dialogue systems more robust.