Papers with predicting labels solely
Towards Debiasing Fact Verification Models (D19-1)
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Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, Regina Barzilay
| Challenge: | Prior research has shown that data collection methods that use crowdsourcing introduce idiosyncratic biases that impact performance in unexpected ways. |
| Approach: | They propose a method to regularize the training data to avoid idiosyncrasies in the datasets that are used for fact verification. |
| Outcome: | The proposed model outperforms the existing model on the FEVER dataset, achieving 61.7% of the baseline. |