  We tested the robustness of sarcasm detection models by examining their
behavior when fine-tuned on four sarcasm datasets containing varying
characteristics of sarcasm: label source (authors vs. third-party), domain
(social media/online vs. offline conversations/dialogues), style (aggressive
vs. humorous mocking). We tested their prediction performance on the same
dataset (intra-dataset) and across different datasets (cross-dataset). For
intra-dataset predictions, models consistently performed better when fine-tuned
with third-party labels rather than with author labels. For cross-dataset
predictions, most models failed to generalize well to the other datasets,
implying that one type of dataset cannot represent all sorts of sarcasm with
different styles and domains. Compared to the existing datasets, models
fine-tuned on the new dataset we release in this work showed the highest
generalizability to other datasets. With a manual inspection of the datasets
and post-hoc analysis, we attributed the difficulty in generalization to the
fact that sarcasm actually comes in different domains and styles. We argue that
future sarcasm research should take the broad scope of sarcasm into account.
