可泛化的讽刺检测当然已近在眼前！
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!
AI总结：
本文在四个特征各异的讽刺数据集上评估检测模型的数据集内与跨数据集性能，发现单一数据集难以覆盖多样讽刺，而新发布数据集可带来最佳泛化。
摘要：
我们通过考察讽刺检测模型在四个包含不同讽刺特征的讽刺数据集上进行微调时的行为，测试了这些模型的鲁棒性；这些特征包括标签来源（作者与第三方）、领域（社交媒体/在线与线下会话/对话）以及风格（攻击性与幽默嘲弄）。我们在同一数据集（数据集内）和跨不同数据集（跨数据集）上测试了它们的预测性能。对于数据集内预测，当使用第三方标签而非作者标签进行微调时，模型始终表现得更好。对于跨数据集预测，大多数模型无法很好地泛化到其他数据集，这表明一种类型的数据集无法代表具有不同风格和领域的各类讽刺。与现有数据集相比，在我们本工作发布的新数据集上微调的模型对其他数据集表现出最高的泛化能力。通过对数据集的人工检查和事后分析，我们将泛化困难归因于讽刺实际上存在不同领域和风格这一事实。我们主张，未来的讽刺研究应考虑讽刺的广泛范围。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
