Weakly-Supervised Scientific Document Classification via
  Retrieval-Augmented Multi-Stage Training
Ran Xu Yue Yu Joyce C. Ho Carl Yang
Scientific document classification is a critical task for a wide range of
applications, but the cost of obtaining massive amounts of human-labeled data
can be prohibitive. To address this challenge, we propose a weakly-supervised
approach for scientific document classification using label names only. In
scientific domains, label names often include domain-specific concepts that may
not appear in the document corpus, making it difficult to match labels and
documents precisely. To tackle this issue, we propose WANDER, which leverages
dense retrieval to perform matching in the embedding space to capture the
semantics of label names. We further design the label name expansion module to
enrich the label name representations. Lastly, a self-training step is used to
refine the predictions. The experiments on three datasets show that WANDER
outperforms the best baseline by 11.9% on average. Our code will be published
at https://github.com/ritaranx/wander.