Abstract
Multi-hop reasoning question answering is a sub-task of machine reading comprehension (MRC) which aims to find the answer of a given question across multiple passages. Most existing models usually obtain the answer by visiting the question only once so that models may not obtain adequate text information. In this paper, we propose a Dynamic Reasoning Network (DRN), a novel approach to obtain correct answers by multi-hop reasoning among multiple passages. We establish a query reshaping mechanism which visits a query repeatedly to mimic people’s reading habit. The model dynamically reasons over an entity graph with graph attention (GAT) and the query reshaping mechanism to promote its ability of comprehension and reasoning. The experimental results on the HotpotQA and TriviaQA datasets show that our DRN model achieves significant improvements as compared to prior state-of-the-art models.
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Acknowledgments
The work was partially supported by the Sichuan Science and Technology Program under Grant Nos. 2018GZDZX0039 and 2019YFG0521.
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Li, X., Liu, Y., Ju, S., Xie, Z. (2020). Dynamic Reasoning Network for Multi-hop Question Answering. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_3
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DOI: https://doi.org/10.1007/978-3-030-60450-9_3
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