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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | When does aggregating multiple skills with multi-task learning work? A case study in financial NLP |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 7465 - 7488 |
| Event Title | 61st Annual Meeting of the Association for Computational Linguistics (ACL’23) |
| Event Type | conference |
| Event Location | Toronto, Canada |
| Event Start Date | July 9 - 2023 |
| Event End Date | July 14 - 2023 |
| Series Name | Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics |
| Number | 1 |
| Place of Publication | Toronto, Canada |
| Publisher | Association for Computational Linguistics |
| Abstract Text | 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 skills relevant to the domain, such as numeric reasoning and sentiment analysis. Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work. Our findings suggest that the key to MTL success lies in skill diversity, relatedness between tasks, and choice of aggregation size and shared capacity. Specifically, MTL works well when tasks are diverse but related, and when the size of the task aggregation and the shared capacity of the model are balanced to avoid overwhelming certain tasks. |
| Free access at | Official URL |
| Official URL | https://aclanthology.org/2023.acl-long.412/ |
| Other Identification Number | merlin-id:23709 |
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