@inproceedings{b2fb817dc21b41f4b3c558eb0d36bad4,
title = "Fine-Grained Argument Understanding with BERT Ensemble Techniques: A Deep Dive into Financial Sentiment Analysis",
abstract = "While argument mining has garnered attention over the years, its application in the financial sector remains nascent. This study presents a BERT-based ensemble learning approach tailored for sentiment analysis grounded in financial narratives, specifically focusing on unearthing arguments. For a nuanced analysis, we dissect the challenge into two pivotal subtasks using earnings conference call data: (1) Argument Unit Classification, and (2) Argument Relation Detection and Classification. Experimental results substantiate that our approach not only effectively forecasts both tasks but also outperforms the comparisons and achieve SOTA performance. This underscores the potential of our method in fine-grained argument understanding within financial analysis.",
keywords = "Ensemble Learning, Financial NLP, Sentiment Analysis",
author = "Eugene Sy and Peng, {Tzu Cheng} and Huang, {Shih Hsuan} and Lin, {Hen You} and Chang, {Yung Chun}",
note = "Publisher Copyright: {\textcopyright} 2023 ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing. All rights reserved.; 35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023 ; Conference date: 20-10-2023 Through 21-10-2023",
year = "2023",
language = "English",
series = "ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "242--249",
editor = "Jheng-Long Wu and Ming-Hsiang Su and Hen-Hsen Huang and Yu Tsao and Hou-Chiang Tseng and Chia-Hui Chang and Lung-Hao Lee and Yuan-Fu Liao and Wei-Yun Ma",
booktitle = "ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing",
}