论文标题
DSC IIT-2020任务8：深度模因情绪分析的双融合技术
DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis
论文作者
论文摘要
模因已成为无处不在的社交媒体实体，并且对这种现象数据的处理和分析目前是一个活跃的研究领域。本文介绍了我们在主体分析方面的工作，共享了2020年Semeval 2020的任务，其中涉及模因的情感和幽默分析。我们提出了一个系统，该系统使用不同的双峰融合技术，即情感和幽默分类任务的模式间依赖性。在我们所有的实验中，最佳系统在幽默分类（任务B）上的宏F1得分为0.357，在幽默分类（任务B）和0.312上提高了基线，而semanticclasses（任务c）的宏观分数为0.510。
Memes have become an ubiquitous social media entity and the processing and analysis of suchmultimodal data is currently an active area of research. This paper presents our work on theMemotion Analysis shared task of SemEval 2020, which involves the sentiment and humoranalysis of memes. We propose a system which uses different bimodal fusion techniques toleverage the inter-modal dependency for sentiment and humor classification tasks. Out of all ourexperiments, the best system improved the baseline with macro F1 scores of 0.357 on SentimentClassification (Task A), 0.510 on Humor Classification (Task B) and 0.312 on Scales of SemanticClasses (Task C).
