We present a novel learning framework for pipeline models aimed at improving the com- munication between consecutive stages in a pipeline. Our method exploits the confidence scores associated with outputs at any given stage in a pipeline in order to compute prob- abilistic features used at other stages down- stream. We describe a simple method of in- tegrating probabilistic features into the linear scoring functions used by state of the art ma- chine learning algorithms. Experimental eval- uation on dependency parsing and named en- tity recognition demonstrate the superiority of our approach over the baseline pipeline mod- els, especially when upstream stages in the pipeline exhibit low accuracy.

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