Detecting Perceived Emotions in Hurricane Disasters
Table 7: Unsupervised domain adaptation accuracies (abbreviations defined in Table 5). Results are highlighted with blue (↑) and red (↓) with respect to Src-Only. Best viewed in color.
['[EMPTY]', 'AGR', 'OPT', 'LOV', 'SBM', 'AWE', 'DSP', 'RMR', 'CNT', 'AVG']
Overall, we do not find a significant increase in performance over the Src-Only baseline. Pre-training consistently adds +1% in average accuracy, but still leaves a large gap between Pretrain-Src and Trg-Only. Regardless, we have a few observations. First, we do not see a (relatively) large increase in performance for submission, awe, disapproval, and remorse. These emotions may need more explicit strategies to enable domain adaptation. Second, Pretrain-Joint performs worse than both Pretrain-Src and Pretrain-Trg. We posit that, for our emotion tasks, pre-training with a mixture of domains yields a noisier training signal compared to a parameter bias towards the target domain.