Enhancing Financial Question Answering with Structured Knowledge

Abstract

Answering questions involving financial documents using a language model requires the ability to recognize tabular and textual data, as well as numerical reasoning. This article explains the challenges, recent progress, and our approach to tackling this problem by incorporating external structured knowledge. We also introduce our financial knowledge graph (KG) linking companies to people, industries, and facts extracted from public financial filings. The KG is part of our work to advance machine-learning models for more complex financial questions beyond the scope of the previous models and datasets.