Contemporary knowledge-based systems increasingly rely on multilingual emotion identification to support intelligent decision-making, yet they face major challenges due to emotional ambiguity and incomplete supervision. Emotion recognition from text is inherently uncertain because multiple emotional states often co-occur and emotion annotations are frequently missing or heterogeneous. Most existing multi-label emotion classification methods assume fully observed labels and rely on deterministic learning objectives, which can lead to biased learning and unreliable predictions under partial supervision. This paper introduces Reasoning under Ambiguity, an uncertainty-aware framework for multilingual multi-label emotion classification that explicitly aligns learning with annotation uncertainty. The proposed approach uses a shared multilingual encoder with language-specific optimization and an entropy-based ambiguity weighting mechanism that down-weights highly ambiguous training instances rather than treating missing labels as negative evidence. A mask-aware objective with positive-unlabeled regularization is further incorporated to enable robust learning under partial supervision. Experiments on English, Spanish, and Arabic emotion classification benchmarks demonstrate consistent improvements over strong baselines across multiple evaluation metrics, along with improved training stability, robustness to annotation sparsity, and enhanced interpretability.

翻译：当代基于知识的系统日益依赖多语言情感识别来支持智能决策，但由于情感模糊性和不完整的监督，它们面临重大挑战。文本情感识别本质上具有不确定性，因为多种情感状态常常同时出现，且情感标注经常缺失或存在异质性。现有的大多数多标签情感分类方法假设标签被完全观测，并依赖于确定性的学习目标，这在部分监督下可能导致有偏学习和不可靠的预测。本文提出了“基于模糊性推理”，这是一个不确定性感知的多语言多标签情感分类框架，它显式地将学习过程与标注不确定性对齐。所提出的方法采用一个共享的多语言编码器，结合语言特定的优化，以及一个基于熵的模糊性加权机制。该机制降低高模糊性训练实例的权重，而不是将缺失标签视为负面证据。此外，框架还整合了一个具有正-未标记正则化的掩码感知目标，以实现部分监督下的鲁棒学习。在英语、西班牙语和阿拉伯语情感分类基准上的实验表明，该方法在多个评估指标上均优于强基线模型，同时展现出改进的训练稳定性、对标注稀疏性的鲁棒性以及增强的可解释性。