Field of Study:

Multi-task Learning (MTL) is a learning paradigm where multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This approach aims to improve the performance of individual tasks by leveraging the shared information among them. It's particularly useful in NLP where datasets can be scarce or imbalanced, as it allows models to learn representations across different tasks, enhancing generalization and robustness.

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