Abstract
Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.
This content is only available as a PDF.
©2016 Association for Computational Linguistics. Distributed
under a CC-BY 4.0 license.
2016
Association for Computational Linguistics
This is an open-access article distributed under the terms of the
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
License, which permits you to copy and redistribute in any medium or format,
for non-commercial use only, provided that the original work is not remixed,
transformed, or built upon, and that appropriate credit to the original
source is given. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.