Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis
Ling Yan, Jianfei Yu, Rui Xia · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 (VLP-MABSA), 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.