Publication: When does aggregating multiple skills with multi-task learning work? A case study in financial NLP
When does aggregating multiple skills with multi-task learning work? A case study in financial NLP
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Leippold, M., Ni, J., Jin, Z., Wang, Q., & Sachan, M. (2023). When does aggregating multiple skills with multi-task learning work? A case study in financial NLP. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 7465–7488. https://aclanthology.org/2023.acl-long.412/
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Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show that MTL performance can be improved by algorithmic tricks. However, what tasks and skills should be included is less well explored. In this work, we conduct a case study in Financial NLP where multiple datasets exist for s
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Leippold, M., Ni, J., Jin, Z., Wang, Q., & Sachan, M. (2023). When does aggregating multiple skills with multi-task learning work? A case study in financial NLP. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 7465–7488. https://aclanthology.org/2023.acl-long.412/
