FinGen: A Dataset for Argument Generation in Finance

TL;DR

FinGen introduces a new dataset for argument generation in finance, highlighting the challenges and unresolved issues in applying current NLP models to this complex task.

Contribution

The paper presents a novel dataset and three argument generation tasks tailored for financial applications, emphasizing the need for further research in this area.

Findings

Current models struggle with financial argument generation

The proposed tasks reveal significant challenges in NLP for finance

Unresolved issues highlight future research directions

Abstract

Thinking about the future is one of the important activities that people do in daily life. Futurists also pay a lot of effort into figuring out possible scenarios for the future. We argue that the exploration of this direction is still in an early stage in the NLP research. To this end, we propose three argument generation tasks in the financial application scenario. Our experimental results show these tasks are still big challenges for representative generation models. Based on our empirical results, we further point out several unresolved issues and challenges in this research direction.