Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
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DOI：
10.18653/v1/2021.naacl-main.2
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Fenia Christopoulou;Makoto Miwa;S. Ananiadou
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文献类型：
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作者：
Fenia Christopoulou;Makoto Miwa;S. Ananiadou
我们提出了一种多任务概率方法，通过拉近包含相同知识库对的句子的表示来促进远程监督关系提取。为了实现这一目标，我们通过与关系分类器联合训练的变分自动编码器（VAE）对句子的潜在空间进行偏置。潜在代码指导配对表示并影响句子重建。通过远程监督创建的两个数据集的实验结果表明，多任务学习会带来性能提升。对在 VAE 中使用知识库先验的进一步探索表明，句子空间可以向知识库的空间转移，提供可解释性并进一步改进结果。
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a relation classifier. The latent code guides the pair representations and influences sentence reconstruction. Experimental results on two datasets created via distant supervision indicate that multi-task learning results in performance benefits. Additional exploration of employing Knowledge Base priors into theVAE reveals that the sentence space can be shifted towards that of the Knowledge Base, offering interpretability and further improving results.