Most of the current SMT systems work at sentence level. They translate a text assuming that sentences are independent, but, when one looks at a well formed document, it is clear that there exist many inter sentence relations. There is much contextual information that, unfortunately, is lost when translating sentences in an independent way. We want to improve translation coherence and cohesion using document level information. So, we are interested in develop new strategies to take advantage of context information to achieve our goal. For example, we want to approach this challenge developing postprocesses in order to try to fix a first translation obtained by an SMT system. Also we are interested in taking advantage of the document level translation framework given by the Docent decoder to implement and test some of our ideas. The analogous problem can be found regarding to automatic MT evaluation metrics because most of them are designed at sentence level so, they do not capture improvements in lexical cohesion and coherence or discourse structure. However, we will left this topic for future work