Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection
Xincheng Ju, Dong Zhang, Rong Xiao, Junhui Li, Shoushan Li, Min Zhang, Guodong Zhou · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental and fine-grained sub-tasks in aspect-level sentiment analysis (ALSA).In the textual analysis, jointly extracting both aspect terms and sentiment polarities has been drawn much attention due to the better applications than individual sub-task.However, in the multimodal scenario, the existing studies are limited to handle each sub-task independently, which fails to model the innate connection between the above two objectives and ignores the better applications.Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).Specifically, we first build an auxiliary text-image relation detection module to control the proper exploitation of visual information.Second, we adopt the hierarchical framework to bridge the multi-modal connection between MATE and MASC, as well as separately visual guiding for each sub module.Finally, we can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects.Extensive experiments show the effectiveness of our approach against the joint textual approaches, pipeline and collapsed multi-modal approaches.