| 09:00 |
Opening |
| 09:10 |
Introduction of the Plan of FinNLP from 2023 to 2025 |
| 09:40 |
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance |
| 10:00 |
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection |
| 10:20 |
A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models |
| 10:40 |
Coffee Break |
| 11:00 |
Beyond Classification: Financial Reasoning in State-of-the-Art Language Models |
| 11:20 |
LoKI: Money Laundering Report Generation via Logical Table-to-Text using Meta Learning |
| 11:40 |
Reducing tokenizer's tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer |
| 12:00 |
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance |
| 12:20 |
Textual Evidence Extraction for ESG Scores |
| 12:40 |
Lunch |
| 14:00 |
Keynote - Dr. Sridhar Dasaratha (Ernst & Young ) |
| 14:30 |
Using Deep Learning to Find the Next Unicorn: A Practical Synthesis on Optimization Target, Feature Selection, Data Split and Evaluation Strategy |
| 14:50 |
DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data |
| 15:10 |
ML-ESG Shared Task Overview |
| 15:20 |
Leveraging Contrastive Learning with BERT for ESG Issue Identification |
| 15:35 |
Coffee Break |
| 16:00 |
Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification |
| 16:15 |
EaSyGuide: ESG Issue Identification Framework leveraging Abilities of Generative Large Language Models |
| 16:30 |
Jetsons at the FinNLP-2023: Using Synthetic Data and Transfer Learning for Multilingual ESG Issue Classification |
| 16:45 |
HKESG at the ML-ESG Task: Exploring Transformer Representations for Multilingual ESG Issue Identification |
| 17:00 |
Team HHU at the FinNLP-2023 ML-ESG Task: A Multi-Model Approach to ESG-Key-Issue Classification |
| 17:15 |
Closing |