TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis
This addresses the challenge of fusing multimodal information for sentiment analysis, offering an incremental improvement over existing methods.
The paper tackled multimodal sentiment analysis by proposing TransModality, an end-to-end fusion method using Transformer to translate between modalities, achieving state-of-the-art performance on datasets like CMU-MOSI, MELD, and IEMOCAP.
Multimodal sentiment analysis is an important research area that predicts speaker's sentiment tendency through features extracted from textual, visual and acoustic modalities. The central challenge is the fusion method of the multimodal information. A variety of fusion methods have been proposed, but few of them adopt end-to-end translation models to mine the subtle correlation between modalities. Enlightened by recent success of Transformer in the area of machine translation, we propose a new fusion method, TransModality, to address the task of multimodal sentiment analysis. We assume that translation between modalities contributes to a better joint representation of speaker's utterance. With Transformer, the learned features embody the information both from the source modality and the target modality. We validate our model on multiple multimodal datasets: CMU-MOSI, MELD, IEMOCAP. The experiments show that our proposed method achieves the state-of-the-art performance.