Database interaction is often characterized as a non-trivial and time- consuming process due to user’s inexperience with the data or the query language. Therefore, there is a need for the databases to be able to “talk back” in order to assist the users during data exploration and eventually lead them to the desired results. In this paper, we tackle the problem of SQL-to-NL by extending the graph-based model of Logos. Our novel extensions include improvements in terms of the system’s translation capabilities and the fluency of the generated explanations. Finally, we report several challenges, highlighted by experiments on different user cases, i.e, astronomy and policy making.