Abstract
Previous work on neural relation extraction mainly focus on point-based methods and ignores uncertainty within the bag, thus making poor predictions when there are insufficient instances of the bag. To solve this problem, in this paper, we propose two density-based methods. Specifically, we assume each bag is a Gaussian distribution and sentences in the bag are drawn from it. We use predicted variance, capturing bag’s uncertainty, as well as predicted mean to draw more samples to enrich one-instance bags. We also use predicted variance to vote for good representation and temper the loss. To the best of our knowledge, this is the first paper to model uncertainty in neural relation extraction. Experiment results on NYT-10 show significant improvements over baselines.
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Acknowledgements
This work is supported by National Key Research and Development Project (No.2020AAA0109302), Shanghai Science and Technology Innovation Action Plan (No.19511120400) and Shanghai Municipal Science and Technology Major Project (No.2021SHZDZX0103).
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Hong, Y., Xiao, Y., Wang, W., Chen, Y. (2022). Modeling Uncertainty in Neural Relation Extraction. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_29
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DOI: https://doi.org/10.1007/978-3-031-00129-1_29
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