Earnings Call Analysis Using a Sparse Attention Based Encoder and Multi-Source Counterfactual Augmentation-Reference-Cited by-同舟云学术

Earnings Call Analysis Using a Sparse Attention Based Encoder and Multi-Source Counterfactual Augmentation

Published:2023-11-25 Issue: Volume: Page:
ISSN:
Container-title:4th ACM International Conference on AI in Finance
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Author:

Yuan Zixuan¹^ORCID,Zhu Yada²^ORCID,Zhang Wei³^ORCID,Xiong Hui⁴^ORCID

Affiliation:

1. FinTech Thrust, Society Hub, Hong Kong University of Science and Technology (Guangzhou), CN

2. MIT-IBM Watson AI Lab, IBM Research, US

3. Xinar.AI, US

4. Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou), CN

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604237.3626839

Reference45 articles.

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2. Dogu Araci . 2019 . Finbert: Financial Sentiment Analysis with Pre-Trained Language Models. arXiv preprint arXiv:1908.10063 (2019). Dogu Araci. 2019. Finbert: Financial Sentiment Analysis with Pre-Trained Language Models. arXiv preprint arXiv:1908.10063 (2019).

3. André Artelt and Barbara Hammer . 2019. On the Computation of Counterfactual Explanations–A Survey. arXiv preprint arXiv:1911.07749 ( 2019 ). André Artelt and Barbara Hammer. 2019. On the Computation of Counterfactual Explanations–A Survey. arXiv preprint arXiv:1911.07749 (2019).

4. Why Attentions May Not Be Interpretable?

5. Iz Beltagy , Matthew E Peters , and Arman Cohan . 2020 . Longformer: The Long-Document Transformer. arXiv preprint arXiv:2004.05150 (2020). Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The Long-Document Transformer. arXiv preprint arXiv:2004.05150 (2020).

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