Abstract
Multi-document question answering is a hot-spot research task that retrieves answers for a natural language question from a set of documents. Although existing models try to enhance the recall ability, the performance of answering short questions is still far from satisfaction. In this paper, we address multi-document question answering by leveraging external knowledge to assist the semantic understanding, improving the overall performance. Specifically, we learn the relationship between questions and candidate paragraphs, and select valuable external knowledge. The machine reading comprehension model is trained to predict the answers in the screened external knowledge, which verifies the role of external knowledge in supplementing the answers. Then keywords are extracted to assist the indexing ability of answering short queries over multiple documents. Gate and Attention components are designed to integrate external knowledge through the deep neural network, assisting the retrieval of answers. We investigate the role of external knowledge in supplementing answers, assisting recall, and automatically assisting of deep neural network in the multi-document question answering system. Experimental results confirm the effectiveness of the proposed method.
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Acknowledgement
This work was supported by National Natural Science Foundation of China (Grant No. 61902074) and Science and Technology Committee Shanghai Municipality (Grant No. 19ZR1404900).
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Yan, Z., Zheng, W. (2022). Multi-document Question Answering Powered by External Knowledge. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_33
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DOI: https://doi.org/10.1007/978-3-031-20891-1_33
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