Incremental Multimodal Sentiment Analysis for HAIs Based on Multitask Active Learning with Interannotator Agreement

Multimodal sentiment analysis (MSA) is critical in developing empathetic and adaptive multimodal dialogue systems or conversational agents that can naturally interact with users by recognizing sentiment and engagement. Addressing the challenges of collecting labeled data for MSA in human-agent interaction (HAI), this study introduces an innovative approach that combines active learning and multitask learning. Our efficient sentiment recognition model leverages active learning to select informative data for learning models, significantly reducing the labor-intensive data labeling process. Furthermore, we employ multitask learning to improve annotation (label) quality by evaluating alignment with true labels and interannotator agreement, thus enhancing the reliability of sentiment annotations. We evaluate the proposed multitask and active learning methods via a human-agent multimodal dialogue dataset that includes various types of sentiment annotations, which are publicly available. The experimental results demonstrate that by learning to predict the agreement score, multitask learning becomes better than singletask learning at capturing the uncertainties in the data. This study lays the groundwork for incremental learning strategies in MSA, aiming to adaptively understand user sentiments in human-agent interactions.