HistRED: A Historical Document-Level Relation Extraction Dataset
Soyoung Yang Minseok Choi Youngwoo Cho Jaegul Choo
Despite the extensive applications of relation extraction (RE) tasks in
various domains, little has been explored in the historical context, which
contains promising data across hundreds and thousands of years. To promote the
historical RE research, we present HistRED constructed from Yeonhaengnok.
Yeonhaengnok is a collection of records originally written in Hanja, the
classical Chinese writing, which has later been translated into Korean. HistRED
provides bilingual annotations such that RE can be performed on Korean and
Hanja texts. In addition, HistRED supports various self-contained subtexts with
different lengths, from a sentence level to a document level, supporting
diverse context settings for researchers to evaluate the robustness of their RE
models. To demonstrate the usefulness of our dataset, we propose a bilingual RE
model that leverages both Korean and Hanja contexts to predict relations
between entities. Our model outperforms monolingual baselines on HistRED,
showing that employing multiple language contexts supplements the RE
predictions. The dataset is publicly available at:
https://huggingface.co/datasets/Soyoung/HistRED under CC BY-NC-ND 4.0 license.