The need for emotional inference from text continues to diversify as more and more disciplines integrate emotions into their theories and applications. These needs include inferring different emotion types, handling multiple languages, and different annotation formats. A shared model between different configurations would enable the sharing of knowledge and a decrease in training costs, and would simplify the process of deploying emotion recognition models in novel environments. In this work, we study how we can build a single model that can transition between these different configurations by leveraging multilingual models and Demux, a transformer-based model whose input includes the emotions of interest, enabling us to dynamically change the emotions predicted by the model. Demux also produces emotion embeddings, and performing operations on them allows us to transition to clusters of emotions by pooling the embeddings of each cluster. We show that Demux can simultaneously transfer knowledge in a zero-shot manner to a new language, to a novel annotation format and to unseen emotions. Code is available at https://github.com/gchochla/Demux-MEmo .

翻译：随着越来越多学科将情感融入其理论与应用中，从文本中推断情感的需求持续多样化。这些需求包括推断不同类型的情感、处理多语言以及不同的标注格式。不同配置间的共享模型能够实现知识共享、降低训练成本，并简化情感识别模型在新环境中的部署流程。本研究探讨如何通过利用多语言模型和Demux（一种基于Transformer的模型，其输入包含目标情感，从而动态调整模型预测的情感）构建一个可在不同配置间迁移的单一模型。Demux还能生成情感嵌入，通过对其执行池化操作，可将嵌入向量聚合为情感簇，实现向情感簇的迁移。实验证明，Demux能够以零样本方式同时将知识迁移至新语言、新型标注格式及未见情感类别。代码已开源：https://github.com/gchochla/Demux-MEmo