End-to-End Bias Mitigation by Modelling Biases in Corpora
Table 2: Results on the SNLI test, hard set, and differences with CE loss. *: results from Belinkov et al. (2019).
['[BOLD] Loss', '[BOLD] BERT  [BOLD] Test', '[BOLD] BERT  [BOLD] Hard', '[BOLD] BERT Δ', '[BOLD] InferSent  [BOLD] Test', '[BOLD] InferSent  [BOLD] Hard', '[BOLD] InferSent Δ']
With InferSent, DFL and PoE result in 4.1 and 4.8 points gain. With BERT, DFL and PoE improve the results by 2.5 and 1.6 absolute points. Compared to the prior work of Belinkov et al. (AdvCls), our PoE model obtains a 7.4 points gain, setting a new state-of-the-art.