CMU MOSEI
Related Papers
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis
AbstractRelated tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs …...
Integrating Multimodal Information in Large Pretrained Transformers
AbstractRecent Transformer-based contextual word representations, including BERT and XLNet, have shown state-of-the-art performance in multiple disciplines within NLP. Fine-tuning the trained contextual models on task-specific datasets has been the key …...
Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language
AbstractMultimodal sentiment analysis aims to learn a joint representation of multiple features. As demonstrated by previous studies, it is shown that the language modality may contain more semantic information than …...
Improving Multimodal fusion via Mutual Dependency Maximisation
AbstractMultimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of channels (i.e visual, acoustic, …...
Towards Speech-only Opinion-level Sentiment Analysis
AbstractThe growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only. Mapping the latter on …...