Building blocks for complex tasks: Robust generative event extraction
  for radiology reports under domain shifts
Sitong Zhou Meliha Yetisgen Mari Ostendorf
This paper explores methods for extracting information from radiology reports
that generalize across exam modalities to reduce requirements for annotated
data. We demonstrate that multi-pass T5-based text-to-text generative models
exhibit better generalization across exam modalities compared to approaches
that employ BERT-based task-specific classification layers. We then develop
methods that reduce the inference cost of the model, making large-scale corpus
processing more feasible for clinical applications. Specifically, we introduce
a generative technique that decomposes complex tasks into smaller subtask
blocks, which improves a single-pass model when combined with multitask
training. In addition, we leverage target-domain contexts during inference to
enhance domain adaptation, enabling use of smaller models. Analyses offer
insights into the benefits of different cost reduction strategies.