FinRED: A Dataset for Relation Extraction in Financial Domain
Soumya Sharma Tapas Nayak Arusarka Bose Ajay Kumar Meena Koustuv Dasgupta Niloy Ganguly Pawan Goyal
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.