Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines
Ramit Sawhney, Mihir Goyal, Prakhar Goel, Puneet Mathur, Rajiv Ratn Shah
Abstract
Risk prediction is an essential task in financial markets. Merger and Acquisition (M&A) calls provide key insights into the claims made by company executives about the restructuring of the financial firms. Extracting vocal and textual cues from M&A calls can help model the risk associated with such financial activities. To aid the analysis of M&A calls, we curate a dataset of conference call transcripts and their corresponding audio recordings for the time period ranging from 2016 to 2020. We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multi-speaker input to forecast the financial risk associated with the M&A calls. Empirical results prove that the task is challenging, with the pro-posed architecture performing marginally better than strong BERT-based baselines. We release the M3A dataset and benchmark models to motivate future research on this challenging problem domain.
BibTeX
@inproceedings{sawhney-etal-2021-multimodal,
title = "Multimodal Multi-Speaker Merger {\&} Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines",
author = "Sawhney, Ramit and
Goyal, Mihir and
Goel, Prakhar and
Mathur, Puneet and
Shah, Rajiv Ratn",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.526/",
doi = "10.18653/v1/2021.acl-long.526",
pages = "6751--6762"
}