FinQA: A Dataset of Numerical Reasoning over Financial Data
Zhiyu Chen | Wenhu Chen | Charese Smiley | Sameena Shah | Iana Borova | Dylan Langdon | Reema Moussa | Matt Beane | Ting-Hao Huang | Bryan Routledge | William Yang Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
The sheer volume of financial statements makes it difficult for humans to access and analyze a business’s financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. We also annotate the gold reasoning programs to ensure full explainability. We further introduce baselines and conduct comprehensive experiments in our dataset. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. Our dataset – the first of its kind – should therefore enable significant, new community research into complex application domains. The dataset and code are publicly available at https://github.com/czyssrs/FinQA.
- Zhiyu Chen 1
- Wenhu Chen 1
- Charese Smiley 1
- Sameena Shah 1
- Iana Borova 1
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