2023年度 人工知能学会全国大会（第37回）
2023年6月6日〜6月9日熊本城ホール(熊本県熊本市) ＋ オンライン
2023年度 人工知能学会全国大会（第37回）
2023年6月6日〜6月9日熊本城ホール(熊本県熊本市) ＋ オンライン
[1U5-IS-2b-03]Enhancing Financial Question Answering with Structured Knowledge
〇Rungsiman Nararatwong1, Natthawut Kertkeidkachorn2, Ziwei Xu1, Ryutaro Ichise3,1(1. National Institute of Advanced Industrial Science and Technology, 2. Japan Advanced Institute of Science and Technology, 3. Tokyo Institute of Technology)
[[Online, Regular]]
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.