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LT3 at SemEval-2020 Task 8: Multi-Modal Multi-Task Learning for Memotion Analysis

机译:在Semeval-2020任务8:Memotion分析的多模态多任务学习的LT3

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Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval-2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates "memebeddings", viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition's baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores.
机译:互联网模型今天已成为社交媒体网络上的非常流行的表达方式。 由文本和图像混合引起的它们的多模态性质使其成为自动分析的非常具有挑战性的研究对象。 在本文中,我们描述了我们对Semeval-2020 Memotion分析任务的贡献。 我们提出了一种多模态多任务学习系统,它包含“MemeBeddings”,Viz。 联合文本和愿景功能,同时为所有三个Memotion子任务学习和优化。 实验结果表明,该系统不断优于竞争的基线,而持续学习的系统设置(任务续定)获得最佳分类F1分数。

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