Multimodal sentiment analysis utilizes various modalities such as Text, Vision and Speech to predict sentiment. As these modalities have unique characteristics, methods have been developed for fusing features. However, the overall modality characteristics are not guaranteed, because traditional fusion methods have some loss of intra-modality and inter-modality. To solve this problem, we introduce a single-stream transformer, All-modalities-in-One BERT (AOBERT). The model is pre-trained on two tasks simultaneously: Multimodal Masked Language Modeling (MMLM) and Alignment Prediction (AP). The dependency and relationship between modalities can be determined using two pre-training tasks. AOBERT achieved state-of-the-art results on the CMU-MOSI, CMU-MOSEI, and UR-FUNNY datasets. Furthermore, ablation studies that validated combinations of modalities, effects of MMLM and AP and fusion methods confirmed the effectiveness of the proposed model.
|Number of pages||9|
|Publication status||Published - 2023 Apr|
Bibliographical noteFunding Information:
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) ( IITP-2017-0-00477 , (SW starlab) Research and development of the high performance in-memory distributed DBMS based on flash memory storage in an IoT environment).
© 2022 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture