REFinD: Relation Extraction Financial Dataset
This addresses a gap for financial applications by providing a domain-specific dataset, though it is incremental as it focuses on data creation rather than method innovation.
The authors tackled the lack of financial-domain datasets for relation extraction by creating REFinD, a large-scale annotated dataset with ~29K instances and 22 relations from financial documents, and found that state-of-the-art models struggle with numeric inference and ambiguity.
A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with $\sim$29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. We also provide an empirical evaluation with various state-of-the-art models as benchmarks for the RE task and highlight the challenges posed by our dataset. We observed that various state-of-the-art deep learning models struggle with numeric inference, relational and directional ambiguity.