Towards Debiasing Sentence Representations
Table 5: We test the effect of Sent-Debias on both single sentence (BERT and ELMo on SST-2, CoLA) and paired sentence (BERT on QNLI) downstream tasks. The performance (higher is better) of debiased BERT and ELMo sentence representations on downstream tasks is not hurt by the debiasing step.
['Test', 'BERT', 'Less biased BERT', 'ELMo', 'Less biased ELMo']
To ensure that debiasing does not hurt the performance on downstream tasks, we report the performance of our debiased BERT and ELMo on SST-2 and CoLA by training a linear classifier on top of debiased BERT sentence representations. However, the performance of ELMo on SST-2 increases slightly from 89.6 to 90.0. We hypothesize that these differences in performance are due to the fact that CoLA tests for linguistic acceptability so it is more concerned with low-level syntactic structure such as verb usage, grammar, and tenses. As a result, changes in sentence representations across bias directions may impact its performance more. For example, sentence representations after the gender debiasing steps may display a mismatch between gendered pronouns and the sentence context. As a result, we do expect possible decreases in accuracy after debiasing. Finally, we test the effect of Sent-Debias on QNLI by training a classifier on top of debiased BERT sentence pair representations. We observe little impact on task performance: our debiased BERT fine-tuned on QNLI achieves 90.6% performance as compared to the 91.3% we obtained without debiasing.