Explainable Earnings Call Representation Learning (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v38i21.30454Keywords:
Representation Learning, Contrastive Learning, Earnings Call Transcript, Information BottleneckAbstract
Earnings call transcripts hold valuable insights that are vital for investors and analysts when making informed decisions. However, extracting these insights from lengthy and complex transcripts can be a challenging task. The traditional manual examination is not only time-consuming but also prone to errors and biases. Deep learning-based representation learning methods have emerged as promising and automated approaches to tackle this problem. Nevertheless, they may encounter significant challenges, such as the unreliability of the representation encoding process and certain domain-specific requirements in the context of finance. To address these issues, we propose a novel transcript representation learning model. Our model leverages the structural information of transcripts to effectively extract key insights, while endowing model with explainability via variational information bottleneck. Extensive experiments on two downstream financial tasks demonstrate the effectiveness of our approach.Downloads
Published
2024-03-24
How to Cite
Huang, Y., Lei, Y., Tai, W., Cheng, Z., Zhong, T., & Zhang, K. (2024). Explainable Earnings Call Representation Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23518-23520. https://doi.org/10.1609/aaai.v38i21.30454
Issue
Section
AAAI Student Abstract and Poster Program