Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
Abstract
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.
BibTeX
@inproceedings{christopoulou-etal-2021-distantly,
title = "Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors",
author = "Christopoulou, Fenia and
Miwa, Makoto and
Ananiadou, Sophia",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.2/",
doi = "10.18653/v1/2021.naacl-main.2",
pages = "11--26"
}