Robust NLP for Finance (RobustFin)

Sameena Shah, Xiaodan Zhu, Gerard de Melo, Armineh Nourbakhsh, Xiaomo Liu, Zhiqiang Ma, Charese Smiley, Zhiyu Chen

Abstract: Natural language processing (NLP) technologies have been widely applied in business domains such as e-commerce and customer service, but their adoption in the financial sector has been constrained by industry-specific performance standards and regulatory restrictions. This challenge has created new opportunities for core research in related areas. Recent advancements in NLP, such as the advent of large language models, has encouraged adoption in the finance sector. However, compared to other domains, finance has stricter requirements for robustness, explainability, and generalizability. Given this background, we propose to organize the first Robust NLP for Finance (RobustFin) workshop at KDD '23 to encourage the study of and research on robustness and explainability technologies with regard to financial NLP. The goal of the workshop is to extend the applications of NLP in finance, while motivating further research in robust NLP.