Federated Learning for Personalized Humor Recognition

Published in ACM Transactions on Intelligent Systems and Technology, 2022

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Xu Guo, Han Yu, Boyang Li, Hao Wang, Pengwei Xing, Siwei Feng, Zaiqing Nie, and Chunyan Miao. 2022. Federated Learning for Personalized Humor Recognition. ACM Trans. Intell. Syst. Technol. 13, 4, Article 68 (August 2022), 18 pages. https://doi.org.remotexs.ntu.edu.sg/10.1145/3511710


author = {Guo, Xu and Yu, Han and Li, Boyang and Wang, Hao and Xing, Pengwei and Feng, Siwei and Nie, Zaiqing and Miao, Chunyan},
title = {Federated Learning for Personalized Humor Recognition},
year = {2022},
issue_date = {August 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {4},
issn = {2157-6904},
url = {https://doi.org/10.1145/3511710},
doi = {10.1145/3511710},
journal = {ACM Trans. Intell. Syst. Technol.},
month = {may},
articleno = {68},
numpages = {18},
keywords = {natural language understanding, Subjectivity, personalization}


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 article, 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.