个性化幽默识别的联邦学习
Federated Learning for Personalized Humor Recognition
摘要：
幽默的计算理解是创造性语言理解和建模的重要课题。它可以在复杂的人机交互中发挥关键作用。这里的挑战是，人类对幽默内容的感知是高度主观的。同一个笑话可能会从不同的读者那里得到不同的有趣程度。这使得幽默识别模型在实际场景中实现个性化具有高度挑战性。现有的方法通常是基于这样的假设，即用户对给定的文本是否幽默有共识。因此，他们不能很好地处理不同的幽默偏好。在本文中，我们提出了FedHumor的方法，通过联邦学习（FL）的个性化方式识别幽默内容。FedHumor扩展了预先训练的语言模型，通过考虑个人幽默偏好的不同分布来指导微调过程。该方法将差异适应策略引入外语学习范式，训练出个性化的幽默识别模型。据我们所知，FedHumor是第一个通过联邦学习实现的基于文本的个性化幽默识别模型。大量的实验证明了FedHumor在识别幽默文本的优势相比，9个国家的最先进的幽默识别方法具有优越的能力，用于处理不同偏好的用户产生的幽默标签的多样性。
英文摘要：
Computational understanding of humor is an important topic under creative language understanding and modeling. It can play a key role in complex human-AI interactions. The challenge here is that human perception of humorous content is highly subjective. The same joke may receive different funniness ratings from different readers. This makes it highly challenging for humor recognition models to achieve personalization in practical scenarios. Existing approaches are generally designed based on the assumption that users have a consensus on whether a given text is humorous or not. Thus, they cannot handle diverse humor preferences well. In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL). Extending a pre-trained language model, FedHumor guides the fine-tuning process by considering diverse distributions of humor preferences from individuals. It incorporates a diversity adaptation strategy into the FL paradigm to train a personalized humor recognition model. To the best of our knowledge, FedHumor is the first text-based personalized humor recognition model through federated learning. Extensive experiments demonstrate the advantage of FedHumor in recognizing humorous texts compared to nine state-of-the-art humor recognition approaches with superior capability for handling the diversity in humor labels produced by users with diverse preferences.
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。
