Token-Level Pun Location Using Multi-Layer BERT with Mixture of Experts

2026-04-12 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A novel two-stage approach for token-level pun location in Portuguese has been developed, addressing the challenge of humor processing in Natural Language Processing. The first stage employs an ensemble of traditional classifiers to filter non-pun sentences, mitigating class imbalance. The second stage integrates a pre-trained BERT encoder with a Mixture-of-Experts (MoE) layer to capture specialized linguistic features for token classification. Validated on the Puntuguese corpus, this method achieved an F-score of 0.74 without requiring post-processing heuristics. Interpretability analyses revealed that the MoE experts specialize in distinct mechanisms, such as punchline detection and morphological patterns, confirming the model's ability to discern humor nuances.

Key takeaway

For research scientists working on humor detection or complex linguistic ambiguity, you should consider a multi-stage approach that combines initial filtering with specialized expert models. This strategy, exemplified by the BERT-MoE architecture, can significantly improve F-scores and provide interpretable insights into how models capture linguistic nuances, especially in under-resourced languages like Portuguese.

Key insights

A two-stage BERT-MoE model effectively locates puns in Portuguese by specializing expert components.

Principles

• Address class imbalance early in NLP pipelines.
• MoE layers can specialize in distinct linguistic features.
• Pre-trained encoders enhance token classification for complex tasks.

Method

A two-stage process: first, ensemble classifiers filter non-pun sentences; second, a BERT encoder with a Mixture-of-Experts layer performs token classification for pun location.

In practice

• Apply ensemble filtering to reduce class imbalance.
• Integrate MoE layers for specialized feature learning.
• Utilize pre-trained BERT for token-level NLP tasks.

Topics

• Token-Level Pun Location
• Natural Language Processing
• BERT Encoder
• Mixture-of-Experts
• Portuguese Language

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.