We present a novel learning framework for pipeline models aimed at improving the communication 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 probabilistic features used at other stages downstream. We describe a simple method of integrating probabilistic features into the linear scoring functions used by state of the art machine learning algorithms. Experimental evaluation on dependency parsing and named entity recognition demonstrate the superiority of our approach over the baseline pipeline models, especially when upstream stages in the pipeline exhibit low accuracy.
Pipeline models using confidence estimates at one stage as weights for further downstream stages improve over baseline dependency parsing and named entity recognition pipeline models REF .
Learning with Probabilistic Features for Improved Pipeline Models
Conference On Empirical Methods In Natural Language Processing
Computer Science