Minimum Bayes Risk based Answer Re-ranking for Question Answering

Nan DUAN

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

This paper presents two minimum Bayes risk (MBR) based Answer Re-ranking (MBRAR) approaches for the question answering (QA) task. The first approach re-ranks single QA system’s outputs by using a traditional MBR model, which measures correlations between answer candidates; while the second approach re-ranks the combined outputs of multiple QA systems with heterogenous answer extraction components by using a mixture model based MBR model. Evaluations are performed on factoid questions selected from two different domains: Web and Jeopardy!, and significant improvements are achieved on all data sets.

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