适配不同交互信息：情感与意图的联合分类
Fitting Different Interactive Information: Joint Classification of Emotion and Intention
AI总结：
针对低资源多模态情感与意图识别任务，提出结合伪标注缓解低资源问题、利用不同注意力头实现两类任务互促并融合提升的方案，在ICASSP MEIJU@2025赛道一测试集获0.5532分并夺冠。
摘要：
本文是ICASSP MEIJU@2025赛道一的第一名解决方案，该赛道聚焦低资源多模态情感与意图识别任务。如何有效利用大量无标注数据，同时保证交互阶段不同难度任务的相互促进，这两点成为竞赛的关键。本文对使用标注数据训练的模型进行伪标注，筛选高置信度样本及其标签以缓解低资源问题。同时利用实验中发现的意图识别易表征的特性，在不同注意力头下与情感识别实现相互促进，并通过融合获得更优的意图识别性能。最终在经过精细化处理的数据上，我们在测试集上取得了0.5532的分数，赢得该赛道冠军。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
