When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
This work addresses the problem of negative transfer in MTL for researchers and practitioners in Financial NLP, but it is incremental as it builds on prior studies with a case study approach.
The study investigated when multi-task learning (MTL) works effectively by aggregating diverse skills like numeric reasoning and sentiment analysis in Financial NLP, finding that success depends on skill diversity, task relatedness, and balanced aggregation size and shared capacity.
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.