Papers by Arshiya Aggarwal
An Empirical Investigation of Bias in the Multimodal Analysis of Financial Earnings Calls (2021.naacl-main)
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| Challenge: | Existing research focuses on textual elements of financial disclosures but ignores the rich acoustic features in the executives’ speech. |
| Approach: | They propose to use a multimodal approach that leverages the verbal and vocal cues of speakers in financial disclosures to predict volatility and risk. |
| Outcome: | The proposed models outperform existing models in the financial realm but still underrepresent the diverse communities spanning demographics, gender, and native speech. |
VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls (2020.emnlp-main)
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| Challenge: | Existing approaches to stock volatility forecasting ignore correlations between stocks. |
| Approach: | They propose to combine vocal cues with verbal and financial cue data to create a multimodal stock volatility prediction model that accounts for stock interdependence via graph convolutions. |
| Outcome: | The proposed model outperforms existing methods showing that it can predict volatility using multimodal learning. |
Towards Robust NLG Bias Evaluation with Syntactically-diverse Prompts (2022.findings-emnlp)
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| Challenge: | Past studies have shown biases in natural language generation systems but there has been little work on evaluating the bias evaluation approaches. |
| Approach: | They propose a method for evaluating biases in natural language generation systems by paraphrasing syntactic prompts with different syntaktic structures and paraphrazing them to evaluate demographic bias. |
| Outcome: | The proposed method is more robust and shows that some syntactic structures prompt more toxic content while others could prompt less biased generation. |