Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module

TL;DR

This paper introduces the CH-SIMS v2.0 dataset and the AV-Mixup Consistent module to enhance multimodal sentiment analysis by emphasizing the importance of acoustic and visual cues, improving model awareness of non-verbal signals.

Contribution

The work presents a new, larger dataset with rich annotations and a novel mixup-based framework to better leverage non-verbal cues in multimodal sentiment analysis.

Findings

CH-SIMS v2.0 doubles the size of the original dataset with additional annotations.

AV-MC framework improves the model's ability to utilize non-verbal cues.

Enhanced interpretability and performance in multimodal sentiment prediction.

Abstract

Multimodal sentiment analysis (MSA), which supposes to improve text-based sentiment analysis with associated acoustic and visual modalities, is an emerging research area due to its potential applications in Human-Computer Interaction (HCI). However, the existing researches observe that the acoustic and visual modalities contribute much less than the textual modality, termed as text-predominant. Under such circumstances, in this work, we emphasize making non-verbal cues matter for the MSA task. Firstly, from the resource perspective, we present the CH-SIMS v2.0 dataset, an extension and enhancement of the CH-SIMS. Compared with the original dataset, the CH-SIMS v2.0 doubles its size with another 2121 refined video segments with both unimodal and multimodal annotations and collects 10161 unlabelled raw video segments with rich acoustic and visual emotion-bearing context to highlight non-verbal cues for sentiment prediction. Secondly, from the model perspective, benefiting from the unimodal annotations and the unsupervised data in the CH-SIMS v2.0, the Acoustic Visual Mixup Consistent (AV-MC) framework is proposed. The designed modality mixup module can be regarded as an augmentation, which mixes the acoustic and visual modalities from different videos. Through drawing unobserved multimodal context along with the text, the model can learn to be aware of different non-verbal contexts for sentiment prediction. Our evaluations demonstrate that both CH-SIMS v2.0 and AV-MC framework enables further research for discovering emotion-bearing acoustic and visual cues and paves the path to interpretable end-to-end HCI applications for real-world scenarios.