Papers by Rungsiman Nararatwong
Enhancing Financial Table and Text Question Answering with Tabular Graph and Numerical Reasoning (2022.aacl-main)
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| Challenge: | Existing models that learn tabular structures in financial documents do not understand tables and numbers. |
| Approach: | They propose to infuse explicit tabular structures through a graph neural network to improve model's performance in question answering. |
| Outcome: | The proposed model outperforms the baseline model in low-resource settings while outperforming the graph module. |
DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (2024.findings-acl)
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| Challenge: | Question answering (QA) is a fundamental task in the field of Natural Language Processing (NLP). |
| Approach: | They propose a database querying and reasoning dataset for question answering that is designed to accommodate sequential questions and multi-hop queries. |
| Outcome: | The proposed dataset better mirrors the dynamics of real-world information retrieval and analysis with a particular focus on the financial reports of US companies. |