Papers by Jochen Leidner
DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents (2022.findings-emnlp)
Copied to clipboard
Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, Dinesh Manocha
| Challenge: | Existing research focuses on textual and audio modalities of financial disclosures but ignores the rich tabular data available in financial reports. |
| Approach: | They propose to combine tabular financial data with text transcripts and audio recordings to improve stock volatility and price movement prediction by 5-12% and reduce gender bias by over 30%. |
| Outcome: | The combined data improves stock volatility and price movement prediction by 5-12% and reduces gender bias caused due to audio-based neural networks by over 30%. |