Federated Learning for Personalized Humor Recognition

TL;DR

This paper introduces FedHumor, a federated learning approach that personalizes humor recognition models by accounting for individual differences in humor perception, outperforming existing methods in handling diverse user preferences.

Contribution

FedHumor is the first text-based personalized humor recognition model utilizing federated learning to adapt to individual humor preferences.

Findings

FedHumor outperforms nine state-of-the-art humor recognition methods.

It effectively handles diverse humor labels from users with different preferences.

The approach demonstrates superior personalization in humor detection tasks.

Abstract

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.