Learning Adaptable Patterns for Passage Reranking

Abstract

This paper proposes passage reranking models that (i) do not require manual fea-ture engineering and (ii) greatly preserve accuracy, when changing application do-main. Their main characteristic is the use of relational semantic structures rep-resenting questions and their answer pas-sages. The relations are established us-ing information from automatic classifiers,