Humor Transfer Learning: Methods & Challenges

Updated 23 October 2025

Humor transfer learning is a computational approach that transfers humor recognition, generation, or translation skills across different tasks, styles, and domains.
It employs diverse datasets and neural architectures—from N-gram models to Transformers—to capture nuanced cultural and multimodal humor cues.
Advanced techniques like ensemble, personalization, and federated learning enable robust cross-domain performance, surpassing traditional binary humor detection.

Humor transfer learning refers to the capacity of computational models or systems—particularly those leveraging neural architectures, transfer learning frameworks, and multimodal pipelines—to generalize, adapt, or transfer humor recognition, generation, or translation capabilities across different tasks, domains, styles, languages, or modalities. Unlike traditional supervised learning narrowly focused on in-domain binary detection or generation, humor transfer learning emphasizes both the portability of representations and mechanisms for capturing the complex, subjective, and multifaceted nature of humor.

1. Problem Formulations and Dataset Diversity

The field has evolved from binary humor classification to more fine-grained formulations such as comparative humor ranking, subjective scoring, cross-cultural adaptation, and multimodal integration. The introduction of novel datasets has catalyzed this shift:

Comparative humor ranking (Potash et al., 2016) requires models to order multiple humorous texts according to funniness, capturing relative judgments essential for nuanced transfer learning scenarios.
Large-scale annotated corpora (e.g., Reddit jokes, short jokes, puns) enable transfer experiments wherein models pre-trained or fine-tuned on one corpus are evaluated on others to measure generalization capability (Weller et al., 2019, Turgeman et al., 26 Aug 2025).
Cross-linguistic and cross-cultural resources (e.g., Spanish HAHA dataset (Miller et al., 2020), Chinese Chumor-2.0 (He et al., 23 Dec 2024)) test the boundaries of transfer by exposing models to culturally specific humor not encountered during source training.
Specialized benchmarks (e.g., HumorBench (Narad et al., 29 Jul 2025)) introduce tasks requiring deep reasoning and multi-hop inference, aligning humor transfer evaluation with mechanisms developed in STEM transfer learning.

2. Architectures and Mechanisms for Transfer

Models used in humor transfer learning span a wide architectural range:

N-gram and LLMs: Early systems employed bigrams/trigrams to compute probabilities over humorous content, yielding strong baselines but struggling with out-of-vocabulary (OOV) phenomena and long-distance dependencies. Performance on transfer is often limited due to fixed vocabularies and local context (Yan et al., 2017).
Character-level encoders: Demonstrated superior performance on puns and neologisms prevalent in digital humor, facilitating transfer to tasks or domains with high surface-level creativity (Potash et al., 2016).
Deep neural models: LSTMs, CNNs, and combinations thereof improve handling of longer contexts and OOV issues. Tree-structured LSTMs and advanced sequence architectures can, in principle, better transfer humor detection across subtasks (Yan et al., 2017, Chaudhary et al., 2021).
Transformer models: Pre-trained BERT-style architectures achieve state-of-the-art performance. Attention mechanisms enable fine-grained localization of humorous elements (e.g., the “laughing head” in BERT attending to crucial tokens) and facilitate transfer across datasets and humor forms (Weller et al., 2019, Peyrard et al., 2021, Li et al., 2022). Cross-dataset transfer without fine-tuning highlights the robustness of learned representations (Weller et al., 2019, Turgeman et al., 26 Aug 2025).
Ensemble interpretable models: Methods such as THInC (Marez et al., 2 Sep 2024) utilize GA²M ensembles, each capturing a distinct humor theory via engineered proxy features, resulting in interpretable transfer across humor taxonomies.

3. Cross-Task, Cross-Style, and Cross-Domain Transfer

Extensive transfer learning experiments have revealed the conditions and limitations of cross-task and cross-style generalization:

Training on diverse humor types leads to robust transfer: LLMs trained on multiple datasets achieve up to 75% accuracy on unseen humor tasks and benefit from training diversity with minimal in-domain performance loss (1.88–4.05%) (Turgeman et al., 26 Aug 2025).
The transfer landscape is asymmetric: Some humor types such as “Dad Jokes” act as strong transfer enablers but are difficult to target (i.e., difficult for models to generalize to without exposure) (Turgeman et al., 26 Aug 2025).
Models trained exclusively on STEM reasoning tasks demonstrate strong transfer to open-domain humor explanation, suggesting that abstract, multi-step hypothesis generation mechanisms facilitate domain-independent humor understanding (Narad et al., 29 Jul 2025).
Multimodal and multilingual transfer: Integrating audio (e.g., for puns) or incorporating external images enables transfer to tasks requiring phonetic or visual cues (Baluja, 1 Dec 2024, Pramanick et al., 2021). Multilingual humor detection and translation frameworks combine chain-of-thought dissection and humor theoretical decomposition to bridge typological and cultural divides (Miller et al., 2020, He et al., 23 Dec 2024, Su et al., 12 Jul 2025).

4. Personalization, Adaptation, and Federated Transfer

Recent advances in personalization and adaptation have addressed challenges arising from subjectivity and user heterogeneity:

Personalized humor recognition (FedHumor (Guo et al., 2020)) combines federated learning with user-specific thresholds, enabling models to transfer knowledge accumulated from diverse individuals while preserving privacy. Diversity adaptation modules correct for user-specific biases, outperforming global or individually fine-tuned models.
Individuality-aware fusion (CIHR (Zhu et al., 7 Feb 2025)) disentangles humor commonality (multi-theory cues) from speaker individuality (static/dynamic user profiles), allowing transfer and adaptation to users with differing humor expression patterns and contextual backgrounds.

5. Mechanistic Insights and Theoretical Integration

The drive for transferability has motivated deeper analysis of what models internalize:

Attention mechanism analysis reveals that transformers can automatically learn to localize humorous tokens ("laughing head") (Peyrard et al., 2021). These insights support the development of explainable style transfer mechanisms.
Theory-driven frameworks (THInC (Marez et al., 2 Sep 2024)) encode superiority, relief, incongruity, and surprise-disambiguation theories via transparent proxy features, enabling interpretable detection and potentially theory-specific transfer across humor types.

6. Applications: Generation, Explanation, and Translation

Humor transfer learning underpins applications that require portable or flexible humor competence:

Generation and explanation: Feedback-driven knowledge distillation (teacher-as-critic) bridges the gap between large and small models for humor generation, outperforming imitation-only baselines by up to 20% in win preferences (Ravi et al., 28 Feb 2024). Hybrid models that combine structured templates and neural infilling demonstrate transfer from classification to controlled punchline generation (Chaudhary et al., 2021).
Translation: Psychology-driven decomposition strategies ensure that models preserve and recompose humor via topic-angle-punchline analysis, using chain-of-thought breakdowns and theory integration to yield improvements in humor quality (+7.75%), fluency (+2.81%), and coherence (+6.13%) over baselines (Su et al., 12 Jul 2025).
Multimodal synthesis: Transfer learning adapts expressive speech models to laughter synthesis, leveraging large speech corpora and subsequent fine-tuning to achieve higher perceived naturalness (MOS = 3.28 vs. 2.5–2.6 for HMM baselines) (Tits et al., 2020).

7. Limitations, Open Questions, and Future Directions

Despite longstanding progress, significant challenges remain for humor transfer learning:

Cultural specificity: LLMs systematically underperform relative to humans in culturally nuanced domains (e.g., Chumor-2.0), highlighting the need for domain-adapted training and evaluation (He et al., 23 Dec 2024).
Evaluation: Standard translation and generation metrics are insufficient; rigorous human or LLM-based rubrics focusing on humor elements and multi-dimensional rating are required (Su et al., 12 Jul 2025, Narad et al., 29 Jul 2025, He et al., 23 Dec 2024).
Generalization: While transfer is feasible, fragmentation persists. Improved approaches for few-shot/meta-learning, active curriculum strategies, and composite, theory-aware loss functions are open avenues (Potash et al., 2016, Marez et al., 2 Sep 2024, Wang et al., 14 Oct 2024).
Modality and prompt sensitivity: Multimodal prompting improves phonetic and contextual humor understanding but remains highly sensitive to configuration, and further work on integrating broader modalities (e.g., gesture, timing, facial cue) is needed (Baluja, 1 Dec 2024).
Strategy optimization: Increasing "thinking tokens" or chain-of-thought reasoning at inference shows mixed benefits and warrants careful calibration per architecture (Narad et al., 29 Jul 2025).

In summary, humor transfer learning is a rapidly advancing field encompassing comparative ranking, feature-rich neural architectures, personalization and federated paradigms, multimodal and multilingual adaptation, theory-grounded interpretable models, and feedback-augmented distillation. Despite challenges related to subjectivity, cultural specificity, and metric inadequacies, empirical evidence shows that general and transferable humor competence—while still bounded—can be achieved by leveraging diversity, architectural insights, and nuanced, theoretically-informed strategies.