FinQA: A Dataset of Numerical Reasoning over Financial Data
The paper introduces a dataset named FinQA, aimed at advancing the field of numerical reasoning within the financial domain. This development addresses the complex nature of financial data analysis by providing a substantial dataset of question-answer (Q&A) pairs constructed by financial experts. The principal contribution of FinQA is its focus on reasoning over both structured tables and unstructured text found within financial reports. Unlike prior datasets rooted in general queries, FinQA brings the challenge of domain-specific complexity to the forefront by incorporating numerical reasoning over rich financial data representations.
Overview of FinQA Dataset
FinQA is composed of 8,281 examples, sourced from earnings reports of S&P 500 companies spanning two decades. Each example includes a question, a corresponding numerical reasoning program, and annotated supporting facts. Notably, the dataset encompasses a diverse range of financial documents, with the questions requiring varying complexities of multi-step reasoning. This divergence from simpler, single-step operations seen in datasets like DROP provides an environment ripe for testing the capabilities of numerical reasoning models in handling real-world financial analyses.
Methodology and Baseline Framework
The authors employ a two-step model framework, FinQANet, which comprises a retriever to identify pertinent data points within a financial report and a generator to produce executable reasoning programs. For retrieval, the model converts table rows into sentence-like structures, allowing for efficient extraction of relevant facts, and employs pre-trained LLMs like BERT for classification. The generator is tasked with creating programs using a Domain-Specific Language (DSL), incorporating both mathematical and table operations, to derive answers from the retrieved data.
Additionally, several baseline methods are assessed, from simple TF-IDF based approaches to pre-trained Longformer applications. The comparisons reveal that FinQANet, particularly with large pre-trained models such as RoBERTa, substantially surpasses simpler and outdated models, although it still lags behind the performance of human experts.
Key Findings
The experiments underscore a substantial performance gap between current NLP models and human experts in complex financial Q&A tasks. FinQANet achieves an execution accuracy of 61.24%, which is higher than non-expert crowdsourcing workers but far from the 91.16% reached by financial domain experts. This discrepancy signals the intricate requirements of numerical reasoning within financial contexts and suggests that existing models need further improvements in incorporating financial knowledge and handling complex multi-step reasoning.
Implications and Future Directions
By establishing a challenging benchmark for numerical reasoning in finance, FinQA serves as a catalyst for future research in neural-symbolic reasoning and domain-specific model refinement. The paper suggests exploring new pre-training tasks focused on specialized domains to narrow the performance gap. Moreover, the intricacies revealed by the dataset—such as understanding financial table structures and unit conversions—call for innovations in the comprehension and reasoning capabilities of NLP models.
In conclusion, FinQA addresses a critical gap in the field of AI by providing a robust dataset aimed at enhancing numerical reasoning over complex financial data. The results and insights presented in the paper offer a pathway for further research, encouraging computational advancements in financial data analysis. With continued exploration and model developments, the FinQA dataset could significantly impact AI's role in automating and improving financial document analysis.