Adversarial shared-private model for cross-domain clinical text entailment recognition

Abstract The recognition of textual entailment (RTE) as the main text understanding task is crucial to the application in biomedical and clinical field, however, the developing of which has been hindered, due to the scarcity of the data annotation. In this work, we propose a domain adaptation framework for the cross-domain clinical RTE. We first construct a hierarchical feature encoder architecture for fully exploring the interactions between the input sentence pair. We then establish shared and private feature extractors based on the feature encoder, for capturing both the domain-specific and domain-invariant features. We further introduce a domain discriminator with the adversarial training algorithm for enhancing the cross-domain transferring. Based on the real-world Chinese dataset, our framework achieves significantly enhanced performances against baseline domain adaptation methods, on the few-shot and zero-shot transferring settings. Further analysis reveals that our model is effective for the cross-domain clinical RTE.