  As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment
Analysis (MABSA) has attracted increasing attention in recent years. However,
previous approaches either (i) use separately pre-trained visual and textual
models, which ignore the crossmodal alignment or (ii) use vision-language
models pre-trained with general pre-training tasks, which are inadequate to
identify finegrained aspects, opinions, and their alignments across modalities.
To tackle these limitations, we propose a task-specific Vision-Language
Pre-training framework for MABSA (VLPMABSA), which is a unified multimodal
encoder-decoder architecture for all the pretraining and downstream tasks. We
further design three types of task-specific pre-training tasks from the
language, vision, and multimodal modalities, respectively. Experimental results
show that our approach generally outperforms the state-of-the-art approaches on
three MABSA subtasks. Further analysis demonstrates the effectiveness of each
pretraining task. The source code is publicly released at
https://github.com/NUSTM/VLP-MABSA.
