DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction
Abhyuday Bhartiya, Kartikeya Badola, Mausam
Abstract
Our goal is to study the novel task of distant supervision for multilingual relation extraction (Multi DS-RE). Research in Multi DS-RE has remained limited due to the absence of a reliable benchmarking dataset. The only available dataset for this task, RELX-Distant (Köksal and Özgür, 2020), displays several unrealistic characteristics, leading to a systematic overestimation of model performance. To alleviate these concerns, we release a new benchmark dataset for the task, named DiS-ReX. We also modify the widely-used bag attention models using an mBERT encoder and provide the first baseline results on the proposed task. We show that DiS-ReX serves as a more challenging dataset than RELX-Distant, leaving ample room for future research in this domain.
BibTeX
@inproceedings{bhartiya-etal-2022-dis,
title = "{D}i{S}-{R}e{X}: A Multilingual Dataset for Distantly Supervised Relation Extraction",
author = "Bhartiya, Abhyuday and
Badola, Kartikeya and
Mausam",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.95/",
doi = "10.18653/v1/2022.acl-short.95",
pages = "849--863"
}