Cognitive salience features enhance multitask deep learning for pragmatic reasoning across cultures

Cross-cultural pragmatic reasoning requires computational systems to interpret implicit meanings and culturally-specific communication patterns beyond literal semantic comprehension. This study proposes a cognitive salience feature-driven multi-task deep learning model that integrates attention-based mechanisms mirroring human perceptual priorities with culture-conditioned gating for cross-cultural pragmatic inference. The model jointly optimizes four pragmatic reasoning tasks-implicature detection, speech act classification, politeness assessment, and cultural appropriateness evaluation-through dynamic task weighting and gradient conflict resolution strategies. Comprehensive experiments conducted on a multilingual corpus spanning eight cultural groups demonstrate that the proposed approach achieves 83.2% overall accuracy and 77.5% cross-cultural transfer accuracy, representing substantial improvements of 9.7 and 12.7% points respectively over standard multi-task learning baselines. Ablation studies confirm that cognitive salience mechanisms contribute 5.2% performance gain, while culture-conditioned processing enhances cross-cultural generalization. The model's learned salience patterns exhibit strong correlation (0.81) with theory-driven pragmatic markers, validating interpretability and theoretical grounding. This work advances computational pragmatics by demonstrating that human-inspired cognitive mechanisms significantly enhance artificial systems' cross-cultural reasoning capabilities.