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        <title>Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis</title>
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    <abstract>As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis (MABSA) has attracted increasing attention inrecent years. However, previous approaches either (i) use separately pre-trained visual and textual models, which ignore the crossmodalalignment or (ii) use vision-language models pre-trained with general pre-training tasks, which are inadequate to identify fine-grainedaspects, opinions, and their alignments across modalities. To tackle these limitations, we propose a task-specific Vision-LanguagePre-training framework for MABSA (VLP-MABSA), which is a unified multimodal encoder-decoder architecture for all the pretrainingand downstream tasks. We further design three types of task-specific pre-training tasks from the language, vision, and multimodalmodalities, 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 pre-training task. The source code is publicly released at https://github.com/NUSTM/VLP-MABSA.</abstract>
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%0 Conference Proceedings
%T Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis
%A Ling, Yan
%A Yu, Jianfei
%A Xia, Rui
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ling-etal-2022-vision
%X As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis (MABSA) has attracted increasing attention inrecent years. However, previous approaches either (i) use separately pre-trained visual and textual models, which ignore the crossmodalalignment or (ii) use vision-language models pre-trained with general pre-training tasks, which are inadequate to identify fine-grainedaspects, opinions, and their alignments across modalities. To tackle these limitations, we propose a task-specific Vision-LanguagePre-training framework for MABSA (VLP-MABSA), which is a unified multimodal encoder-decoder architecture for all the pretrainingand downstream tasks. We further design three types of task-specific pre-training tasks from the language, vision, and multimodalmodalities, 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 pre-training task. The source code is publicly released at https://github.com/NUSTM/VLP-MABSA.
%R 10.18653/v1/2022.acl-long.152
%U https://aclanthology.org/2022.acl-long.152
%U https://doi.org/10.18653/v1/2022.acl-long.152
%P 2149-2159

Markdown (Informal)

[Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis](https://aclanthology.org/2022.acl-long.152) (Ling et al., ACL 2022)

• Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis (Ling et al., ACL 2022)

ACL

• Yan Ling, Jianfei Yu, and Rui Xia. 2022. Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2149–2159, Dublin, Ireland. Association for Computational Linguistics.