Distributionally Robust Classifiers in Sentiment Analysis

2021-10-20 · Shilun Li, Renee Li, Carina Zhang

In this paper, we propose sentiment classification models based on BERT integrated with DRO (Distributionally Robust Classifiers) to improve model performance on datasets with distributional shifts. We added 2-Layer Bi-LSTM, projection layer (onto simplex or Lp ball), and linear layer on top of BERT to achieve distributionally robustness. We considered one form of distributional shift (from IMDb dataset to Rotten Tomatoes dataset). We have confirmed through experiments that our DRO model does improve performance on our test set with distributional shift from the training set.

📄 PDF Abstract BibTeX arXiv:2110.10372

Code (1)

Shilun-Allan-Li/DRO-BERT 공식 구현 pytorch

Tasks

Sentiment AnalysisSentiment Classification

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