  Cross-task knowledge transfer via multi-task learning has recently made
remarkable progress in general NLP tasks. However, entity tracking on the
procedural text has not benefited from such knowledge transfer because of its
distinct formulation, i.e., tracking the event flow while following structural
constraints. State-of-the-art entity tracking approaches either design
complicated model architectures or rely on task-specific pre-training to
achieve good results. To this end, we propose MeeT, a Multi-task
learning-enabled entity Tracking approach, which utilizes knowledge gained from
general domain tasks to improve entity tracking. Specifically, MeeT first
fine-tunes T5, a pre-trained multi-task learning model, with entity
tracking-specialized QA formats, and then employs our customized decoding
strategy to satisfy the structural constraints. MeeT achieves state-of-the-art
performances on two popular entity tracking datasets, even though it does not
require any task-specific architecture design or pre-training.
