使事件提取器适应医疗数据：桥接协变量偏移
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift
摘要：
我们解决的任务，适应事件提取器的新领域没有标记的数据，通过对齐的边缘分布的源和目标域。作为测试平台，我们使用来自两个医学领域的英语文本创建了两个新的事件提取数据集：（i）临床笔记，以及（ii）医患对话。我们测试了三种边缘对齐技术的有效性：（i）对抗性领域自适应（ADA），（ii）领域自适应微调（DAFT），以及（iii）基于语言模型似然分数（LIW）的新实例加权技术。LIW和DAFT在两个领域的无转移BERT基线上均有所改善，但ADA仅在临床记录上有所改善。更深入地分析不同类型的轮班（例如，词汇转移，语义转移）揭示了模型之间有趣的变化。我们表现最好的模型在笔记和对话上分别达到了70.0和72.9的F1分数，没有使用来自目标领域的标记数据。
英文摘要：
We tackle the task of adapting event extractors to new domains without labeled data, by aligning the marginal distributions of source and target domains. As a testbed, we create two new event extraction datasets using English texts from two medical domains: (i) clinical notes, and (ii) doctor-patient conversations. We test the efficacy of three marginal alignment techniques: (i) adversarial domain adaptation (ADA), (ii) domain adaptive fine-tuning (DAFT), and (iii) a novel instance weighting technique based on language model likelihood scores (LIW). LIW and DAFT improve over a no-transfer BERT baseline on both domains, but ADA only improves on clinical notes. Deeper analysis of performance under different types of shifts (e.g., lexical shift, semantic shift) reveals interesting variations among models. Our best-performing models reach F1 scores of 70.0 and 72.9 on notes and conversations respectively, using no labeled data from target domains.
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