解码多模态仇恨梗图的深层含义
Decoding the Underlying Meaning of Multimodal Hateful Memes
AI总结：
本文提出HatReD数据集及条件生成任务，自动生成仇恨梗图的深层原因解释，并建立基线性能，助力内容审核。
摘要：
近期研究提出了在仇恨梗图分类任务中表现良好的模型。然而，这些模型并未生成可解释的说明来揭示深层含义并支持分类输出。缺乏可解释的仇恨梗图方法的一个主要原因是缺少包含用于基准测试或训练的真实解释的仇恨梗图数据集。直观地说，拥有此类解释可以教育和帮助内容审核员解读和移除被标记的仇恨梗图。本文通过引入带有原因数据集（HatReD）的仇恨梗图来填补这一研究空白，这是一个新的多模态仇恨梗图数据集，标注了潜在的仇恨语境原因。我们还定义了一个新的条件生成任务，旨在自动生成潜在原因以解释仇恨梗图，并在此任务上建立了最先进的预训练语言模型的基线性能。我们进一步通过分析新条件生成任务在解释已见和未见领域中的梗图时所面临的挑战，证明了HatReD的实用性。数据集和基准模型可在以下网址获取：https://github.com/Social-AI-Studio/HatRed
英文摘要：
Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme dataset annotated with the underlying hateful contextual reasons. We also define a new conditional generation task that aims to automatically generate underlying reasons to explain hateful memes and establish the baseline performance of state-of-the-art pre-trained language models on this task. We further demonstrate the usefulness of HatReD by analyzing the challenges of the new conditional generation task in explaining memes in seen and unseen domains. The dataset and benchmark models are made available here: https://github.com/Social-AI-Studio/HatRed
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。
