The high-level adaptation process involves automatically weighting and combining multiple translation resources, according to several criteria, in order to better match a target corpus or a specific domain sample. The criteria we examine include lexical-level domain match, translation quality estimates, size, and taxonomy representation. An orthogonal dimension in the adaptation process is the granularity level at which these criteria are measured and applied: from the collection level - under the assumption of homogeneous within-collection data - to the document level. The relative contribution of each criterion is subsequently determined by a model that can range from uniform weighting to a global non-linear optimization model trained on application specific evaluation data. 
