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Cognitive salience features enhance multitask deep learning for pragmatic reasoning across cultures
1Faculty of Modern Languages and Communication, University Putra Malaysia, Serdang, 43400, Malaysia. qimeng202406@163.com.
Scientific Reports
|March 2, 2026
Summary
This study introduces a novel deep learning model for cross-cultural pragmatic reasoning, significantly improving accuracy in understanding implicit meanings across diverse cultures. The approach enhances artificial systems' ability to interpret culturally nuanced communication.
Area of Science:
- Computational Linguistics
- Artificial Intelligence
- Cognitive Science
Background:
- Cross-cultural communication presents challenges for AI due to implicit meanings and cultural nuances.
- Existing computational pragmatic models often struggle with cultural variations.
Purpose of the Study:
- To develop a deep learning model for enhanced cross-cultural pragmatic reasoning.
- To integrate cognitive salience and culture-conditioned mechanisms for improved AI communication.
Main Methods:
- A cognitive salience feature-driven multi-task deep learning model was proposed.
- The model incorporates attention mechanisms and culture-conditioned gating.
- Joint optimization of implicature detection, speech act classification, politeness assessment, and cultural appropriateness evaluation was performed.
Main Results:
- The model achieved 83.2% overall accuracy and 77.5% cross-cultural transfer accuracy.
- Significant performance gains of 9.7% and 12.7% over baselines were observed.
- Cognitive salience and culture-conditioned processing were validated through ablation studies.
Conclusions:
- Human-inspired cognitive mechanisms substantially improve AI's cross-cultural reasoning capabilities.
- The model demonstrates enhanced interpretability and theoretical grounding in computational pragmatics.
- This work advances AI's ability to navigate complex cross-cultural communication.
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