Detecting Perceived Emotions in Hurricane Disasters
Table 9: Pre-training using multi-class and binary EmoNet tasks. See Table 6 for styling considerations.
['[EMPTY]', 'AGR', 'OPT', 'LOV', 'SBM', 'AWE', 'DSP', 'RMR', 'CNT', 'AVG']
In this section, we explore a fine-grained pre-training scheme. We create Plutchik-8 binary tasks from EmoNet, then fine-tune each emotion model separately on their respective HurricaneEmo tasks. EmoNet-Binary performs markedly worse than EmoNet-Multi and leads to a -2% reduction in averaged accuracy. Therefore, multi-class pre-training creates better representations for downstream evaluation, although they are still not as effective as other pre-training methods (e.g., masked language modeling).