情感识别中的迁移学习问题综述

情感识别是实现自然人机交互的必要过程。然而，情感数据高昂的采集和标注成本成为了限制情感识别研究发展的一大瓶颈。在无标注或有限标注的场景下，利用知识的跨领域或跨任务迁移提升情感识别效果的问题值得探索。本文对情感识别中的迁移学习问题进行了梳理和分析。首先，将迁移学习问题划分为针对领域差异和针对任务差异的两大部分，并进一步将每部分问题细分为多种不同的情况。随后，基于情感识别领域的研究现状，分别总结不同情况下的现有工作。在目标领域训练资源匮乏的情况下，可以利用其他带标注的数据集作为源领域训练模型，并对齐不同领域下的特征分布，或将特征映射到域间共享的空间。考虑到情感标签所提供的监督信息往往较为有限，为了进一步提升模型的识别效果，可以引入其他相关任务进行联合训练，或将预训练模型、外部知识库提供的先验语义知识迁移到情感识别任务中。最后，讨论了情感识别领域中未来需要得到更多关注和探索的迁移学习问题，旨在为研究者带来新的启发。

A Survey of Transfer Learning Problems in Emotion Recognition

? ?Emotion recognition is an essential process of natural human-computer interaction. However， the high cost of data collection and labeling has become a bottleneck in the development of emotion recognition research. It is worth exploring how to improve the recognition performance by the cross-domain or cross-task knowledge transfer in scenarios with no or limited annotations. This paper organizes and analyzes transfer learning problems in emotion recognition. Firstly， transfer learning problems are divided into two parts， which are problems for domain discrepancies and for task differences. Each part of them is further subdivided into several different situations. Then， existing works of emotion recognition in different situations are summarized respectively. In the case of scarce training resources in the target domain， other annotated datasets can be used as the source domain to train the model. During this process， the feature distributions from different domains should be aligned， or the features should be mapped to a shared space. Considering that the supervision information provided by emotion annotations is often limited， in order to further improve the recognition performance of the model， other related tasks can be introduced for joint training， or the prior semantic knowledge provided by the pre-training model and external knowledge base can be transferred to the emotion recognition task. Finally， transfer learning problems in the emotion recognition task which need more attention and exploration in the future are discussed， aiming to bring new inspiration to researchers.