论文标题
使用多任务学习从C-Spine放射学报告中有效提取病理学
Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning
论文作者
论文摘要
在特定领域的CORPORA上进行了预处理的基于变压器的模型已改变了NLP的景观。通常，如果一个人在给定数据集上具有多个任务，则可以使用不同的模型或使用特定任务适配器。在这项工作中，我们表明，多任务模型可以在各种任务和各种特定任务适配器增强基于BERT的模型上击败或实现多个基于BERT的模型的性能。我们验证了有关颈椎内部放射科医生报告数据集的方法。我们假设这些任务在语义上是紧密的且相关的，因此多任务学习者是强大的分类器。我们的工作打开了使用我们的方法来放射科医生在各个身体部位的报告的范围。
Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. Generally, if one has multiple tasks on a given dataset, one may finetune different models or use task specific adapters. In this work, we show that a multi-task model can beat or achieve the performance of multiple BERT-based models finetuned on various tasks and various task specific adapter augmented BERT-based models. We validate our method on our internal radiologist's report dataset on cervical spine. We hypothesize that the tasks are semantically close and related and thus multitask learners are powerful classifiers. Our work opens the scope of using our method to radiologist's reports on various body parts.
