2022
DOI: 10.48550/arxiv.2206.09591
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Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Abstract: Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to new domains. Existing works usually fails to leverage the implicit relationships among words across domains. In this paper, we propose a novel method, called Domain Adaptation with Structured Knowledge (DASK), to enhance domain adaptation by exploiting word-level semantic relationships. DASK first builds a knowledge graph to capture… Show more

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