Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2204.07955v2
• Date: Thu, 21 Apr 2022 12:46:38 GMT
• Title: Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis
• Authors: Yan Ling, Jianfei Yu, Rui Xia
• Abstract summary: Multimodal Aspect-Based Sentiment Analysis (MABSA) has attracted increasing attention in recent years. Previous approaches either (i) use separately pre-trained visual and textual models, which ignore the crossmodal alignment or (ii) use vision-grained models pre-trained with general pre-training tasks. We propose a task-specific Vision-Language Pre-training framework for MABSA (MABSA), which is a unified multimodal encoder-decoder architecture for all the pretraining and downstream tasks.
• Score: 25.482853330324748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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