Employing sentence context in czech answer selection

Abstract

Question answering (QA) of non-mainstream languages requires specific adaptations of the current methods tested primarily with very large English resources. In this paper, we present the results of improving the QA answer selection task by extending the input candidate sentence with selected information from preceding sentence context. The described model represents the best published answer selection model for the Czech language as an example of a morphologically rich language. The text contains thorough evaluation of the new method including model hyperparameter combinations and detailed error discussion. The winning models have improved the previous best results by 4% reaching the mean average precision of 82.91%.