利用伪标签与注意力机制融合，提升低资源下情感与意图识别性能
Fitting Different Interactive Information: Joint Classification of Emotion and Intention
- 基于高置信度伪标签扩充数据，缓解标注资源不足问题
- 通过注意力头间互促机制，使意图识别准确率显著提升
- 适合低资源多模态情感意图联合识别任务的研究者参考
本文是ICASSP MEIJU@2025 Track I的冠军解决方案，聚焦低资源多模态情感与意图识别。针对如何有效利用大量未标注数据，以及在交互阶段实现不同难度任务间的相互促进这一核心挑战，提出方法：先对有标签数据训练的模型进行伪标签生成，筛选高置信度样本以缓解资源短缺；实验发现意图识别在模型中具有更强表征能力，因此设计不同注意力头间的互促机制，实现情感与意图的协同优化。最终在经过精炼处理的数据上，测试集得分达0.5532，获得该赛道第一名。
原文摘要 · Abstract (English)
This paper is the first-place solution for ICASSP MEIJU@2025 Track I, which focuses on low-resource multimodal emotion and intention recognition. How to effectively utilize a large amount of unlabeled data, while ensuring the mutual promotion of different difficulty levels tasks in the interaction stage, these two points become the key to the competition. In this paper, pseudo-label labeling is carried out on the model trained with labeled data, and samples with high confidence and their labels are selected to alleviate the problem of low resources. At the same time, the characteristic of easy represented ability of intention recognition found in the experiment is used to make mutually promote with emotion recognition under different attention heads, and higher performance of intention recognition is achieved through fusion. Finally, under the refined processing data, we achieve the score of 0.5532 in the Test set, and win the championship of the track.
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