Abstract
Answer ranking is one of essential steps in open domain question answering systems. The ranking of the retrieved answers directly affects user satisfaction. This paper proposes a new joint model for answer ranking by leveraging context semantic features, which balances both question-answer similarities and answer ranking scores. A publicly available dataset containing 40,000 Chinese questions and 369,919 corresponding answer passages from Sogou Lab is used for experiments. Evaluation on the joint model shows a Precison@1 of 72.6%, which outperforms the state-of-the-art baseline methods.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No.61772146), the OUHK 2018/19 S&T School Research Fund (R5077), and Natural Science Foundation of Guangdong Province (2018A030310051).
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Xie, W., Wong, LP., Lee, LK., Au, O., Hao, T. (2020). A Semantic Expansion-Based Joint Model for Answer Ranking in Chinese Question Answering Systems. In: Wang, F., et al. Information Retrieval Technology. AIRS 2019. Lecture Notes in Computer Science(), vol 12004. Springer, Cham. https://doi.org/10.1007/978-3-030-42835-8_3
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