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- W3177101259 abstract "Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning. However, existing methods indiscriminately use the recognized patterns in the testing phase that is inherently different from us humans who have counterfactual thinking, e.g., to scrutinize for the hard testing samples. Inspired by this, we propose a Counterfactual Reasoning Model, which mimics the counterfactual thinking by learning from few counterfactual samples. In particular, we devise a generation module to generate representative counterfactual samples for each factual sample, and a retrospective module to retrospect the model prediction by comparing the counterfactual and factual samples. Extensive experiments on sentiment analysis (SA) and natural language inference (NLI) validate the effectiveness of our method." @default.
- W3177101259 created "2021-07-05" @default.
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- W3177101259 date "2021-01-01" @default.
- W3177101259 modified "2023-10-14" @default.
- W3177101259 title "Empowering Language Understanding with Counterfactual Reasoning" @default.
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- W3177101259 doi "https://doi.org/10.18653/v1/2021.findings-acl.196" @default.
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