  In joint entity and relation extraction, existing work either sequentially
encode task-specific features, leading to an imbalance in inter-task feature
interaction where features extracted later have no direct contact with those
that come first. Or they encode entity features and relation features in a
parallel manner, meaning that feature representation learning for each task is
largely independent of each other except for input sharing. We propose a
partition filter network to model two-way interaction between tasks properly,
where feature encoding is decomposed into two steps: partition and filter. In
our encoder, we leverage two gates: entity and relation gate, to segment
neurons into two task partitions and one shared partition. The shared partition
represents inter-task information valuable to both tasks and is evenly shared
across two tasks to ensure proper two-way interaction. The task partitions
represent intra-task information and are formed through concerted efforts of
both gates, making sure that encoding of task-specific features is dependent
upon each other. Experiment results on six public datasets show that our model
performs significantly better than previous approaches. In addition, contrary
to what previous work has claimed, our auxiliary experiments suggest that
relation prediction is contributory to named entity prediction in a
non-negligible way. The source code can be found at
https://github.com/Coopercoppers/PFN.
