FinRED: A Dataset for Relation Extraction in Financial Domain
Abstract
Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets. In the current literature, there is no extensive open-source relation extraction dataset specific to the finance domain. In this paper, we release FinRED, a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. FinRED has been created by mapping Wikidata triplets using distance supervision method. We manually annotate the test data to ensure proper evaluation. We also experiment with various state-of-the-art relation extraction models on this dataset to create the benchmark. We see a significant drop in their performance on FinRED compared to the general relation extraction datasets which tells that we need better models for financial relation extraction.
Keywords
Cite
@article{arxiv.2306.03736,
title = {FinRED: A Dataset for Relation Extraction in Financial Domain},
author = {Soumya Sharma and Tapas Nayak and Arusarka Bose and Ajay Kumar Meena and Koustuv Dasgupta and Niloy Ganguly and Pawan Goyal},
journal= {arXiv preprint arXiv:2306.03736},
year = {2023}
}
Comments
Accepted at FinWeb at WWW'22