Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors

• DOI: 10.18653/v1/2021.naacl-main.2
• Publication Date: 2021-06-13T01:24:51Z
• Authors: Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Computation and Language (cs.CL)

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 the VAE reveals that the sentence space can be shifted towards that of the Knowledge Base, offering interpretability and further improving results.<br/>16 pages, 9 figures, Accepted as a long paper at NAACL 2021<br/>