Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition
This addresses the challenge of handling unseen or low-resource emotions in multimodal emotion recognition, which is incremental as it builds on existing embedding and mapping techniques.
The paper tackled the problems of underutilized relationships between emotion categories and poor performance on low-resource or unseen emotions in multimodal emotion recognition by proposing a modality-transferable model with emotion embeddings, achieving state-of-the-art performance on most categories and outperforming baselines in zero-shot and few-shot scenarios.
Despite the recent achievements made in the multi-modal emotion recognition task, two problems still exist and have not been well investigated: 1) the relationship between different emotion categories are not utilized, which leads to sub-optimal performance; and 2) current models fail to cope well with low-resource emotions, especially for unseen emotions. In this paper, we propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. We use pre-trained word embeddings to represent emotion categories for textual data. Then, two mapping functions are learned to transfer these embeddings into visual and acoustic spaces. For each modality, the model calculates the representation distance between the input sequence and target emotions and makes predictions based on the distances. By doing so, our model can directly adapt to the unseen emotions in any modality since we have their pre-trained embeddings and modality mapping functions. Experiments show that our model achieves state-of-the-art performance on most of the emotion categories. In addition, our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.