Summary of Rirag: Regulatory Information Retrieval and Answer Generation, by Tuba Gokhan and Kexin Wang and Iryna Gurevych and Ted Briscoe
RIRAG: Regulatory Information Retrieval and Answer Generation
by Tuba Gokhan, Kexin Wang, Iryna Gurevych, Ted Briscoe
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Regulatory Natural Language Processing (RegNLP) aims to simplify access to and interpretation of regulatory rules. We introduce the task of generating question-passage pairs for regulatory question-answering systems. Our ObliQA dataset contains 27,869 questions derived from Abu Dhabi Global Markets (ADGM) financial regulation documents. We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it using RePASs, a novel evaluation metric that assesses the accuracy of generated answers in capturing relevant obligations while avoiding contradictions. This work facilitates the development of regulatory question-answering systems, which can aid organizations in ensuring legal compliance with changing regulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Regulatory documents are hard to understand and take up a lot of time and expertise to follow. Regulatory Natural Language Processing (RegNLP) is trying to make it easier for people to access and interpret these rules. We created a big dataset called ObliQA with questions about financial regulations from Abu Dhabi Global Markets. We built a basic system that answers questions based on the regulations, and we tested it using a new way of measuring how well it does. This can help organizations stay compliant with changing laws. |
Keywords
» Artificial intelligence » Natural language processing » Question answering
