Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts

• URL: http://arxiv.org/abs/2306.09544v1
• Date: Thu, 15 Jun 2023 23:16:58 GMT
• Title: Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts
• Authors: Sitong Zhou, Meliha Yetisgen, Mari Ostendorf
• Abstract summary: We show 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.
• Score: 11.845850292404768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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