Learning to Rank for Plausible Plausibility
Table 2: Results on recast MNLI and JOCI.
['Dataset', 'Log loss', 'Margin loss']
We find that for the two synthetic MNLI datasets, margin-loss performs similarly to cross entropy log-loss. Shifting to the JOCI datasets, with less extreme (contradiction / entailed) hypotheses, especially in the adversarial JOCI2 variant, margin-loss outperforms log-loss.