1 research outputs found
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift
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