  The existence of multiple datasets for sarcasm detection prompts us to apply
transfer learning to exploit their commonality. The adversarial neural transfer
(ANT) framework utilizes multiple loss terms that encourage the source-domain
and the target-domain feature distributions to be similar while optimizing for
domain-specific performance. However, these objectives may be in conflict,
which can lead to optimization difficulties and sometimes diminished transfer.
We propose a generalized latent optimization strategy that allows different
losses to accommodate each other and improves training dynamics. The proposed
method outperforms transfer learning and meta-learning baselines. In
particular, we achieve 10.02% absolute performance gain over the previous state
of the art on the iSarcasm dataset.
