@inproceedings{jain-etal-2024-saliency-aware,
title = "Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction",
author = "Jain, Samyak and
Chhabra, Parth and
Neerkaje, Atula Tejaswi and
Mathur, Puneet and
Sawhney, Ramit and
Agarwal, Shivam and
Nakov, Preslav and
Chava, Sudheer and
Manocha, Dinesh",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1244",
pages = "14285--14297",
abstract = "Predicting price variations of financial instruments for risk modeling and stock trading is challenging due to the stochastic nature of the stock market. While recent advancements in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, limitations exist. Most datasets are small, and show domain distribution shifts due to the nature of their source, suggesting the exploration for data augmentation for robust augmentation strategies such as Mixup. To tackle such challenges in the financial domain, we propose SH-Mix: Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. SH-Mix combines multi-level embedding mixup strategies based on the contribution of each modality and context subsequences. Through extensive quantitative and qualitative experiments on financial earnings and conference call datasets consisting of text and speech, we show that SH-Mix outperforms state-of-the-art methods by 3-7{\%}. Additionally, we show that SH-Mix is generalizable across different modalities and models.",
}
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<abstract>Predicting price variations of financial instruments for risk modeling and stock trading is challenging due to the stochastic nature of the stock market. While recent advancements in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, limitations exist. Most datasets are small, and show domain distribution shifts due to the nature of their source, suggesting the exploration for data augmentation for robust augmentation strategies such as Mixup. To tackle such challenges in the financial domain, we propose SH-Mix: Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. SH-Mix combines multi-level embedding mixup strategies based on the contribution of each modality and context subsequences. Through extensive quantitative and qualitative experiments on financial earnings and conference call datasets consisting of text and speech, we show that SH-Mix outperforms state-of-the-art methods by 3-7%. Additionally, we show that SH-Mix is generalizable across different modalities and models.</abstract>
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%0 Conference Proceedings
%T Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction
%A Jain, Samyak
%A Chhabra, Parth
%A Neerkaje, Atula Tejaswi
%A Mathur, Puneet
%A Sawhney, Ramit
%A Agarwal, Shivam
%A Nakov, Preslav
%A Chava, Sudheer
%A Manocha, Dinesh
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F jain-etal-2024-saliency-aware
%X Predicting price variations of financial instruments for risk modeling and stock trading is challenging due to the stochastic nature of the stock market. While recent advancements in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, limitations exist. Most datasets are small, and show domain distribution shifts due to the nature of their source, suggesting the exploration for data augmentation for robust augmentation strategies such as Mixup. To tackle such challenges in the financial domain, we propose SH-Mix: Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. SH-Mix combines multi-level embedding mixup strategies based on the contribution of each modality and context subsequences. Through extensive quantitative and qualitative experiments on financial earnings and conference call datasets consisting of text and speech, we show that SH-Mix outperforms state-of-the-art methods by 3-7%. Additionally, we show that SH-Mix is generalizable across different modalities and models.
%U https://aclanthology.org/2024.lrec-main.1244
%P 14285-14297
Markdown (Informal)
[Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction](https://aclanthology.org/2024.lrec-main.1244) (Jain et al., LREC-COLING 2024)
ACL
- Samyak Jain, Parth Chhabra, Atula Tejaswi Neerkaje, Puneet Mathur, Ramit Sawhney, Shivam Agarwal, Preslav Nakov, Sudheer Chava, and Dinesh Manocha. 2024. Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14285–14297, Torino, Italia. ELRA and ICCL.