CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1100–1105 Barcelona, Spain (Online), December 12, 2020. 1100 CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT Zhen Li, Yaojie Zhang Harbin Institute of Technology linklizhen@163.com yjzhang@hit-mtlab.net Bing Xu, Tiejun Zhao Harbin Institute of Technology hitxb@hit.edu.cn tjzhao@hit.edu.cn Abstract Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and data contains noise. We use RandAugment to enhance the data of the picture, and use Training Signal Annealing (TSA) to solve the impact of the imbalance of the label. At the same time, a new loss function is designed to ensure that the model is not affected by input noise which will improve the robustness of the model. We participated in sub-task a and our model based on BERT obtains 34.58% macro F1 score, ranking 10/32. 1 Introduction Memes are from people’s culture or some social activities in daily life, usually composed of one or two forms of image, video, gif, and text (Park, 2020). Memes are active in people’s social media, but with the number of memes increasing, offensive memes are also increasing (Williams et al., 2016). So for many social media companies, how to identify the type of meme is very important. Using machine learning to identify the type of meme is a very important solution, and has shown promising Performance. In SemEval-2020 Task 8: Memotion Analysis (Sharma et al., 2020), the organizer collecting the Internet memes and then using OCR recognition it and correct manually the text on the pictures. The task is divided into three sub-tasks: a) sub-task a detects the emotions of the Internet memes, which are divided into positive, negative and neutral; b) sub-task b detects the types of humor expressed by Internet memes, which are divided into sarcastic, humorous, offensive and motivation type, a meme can belong to multiple categories; c) sub-task c is a semantic level classification of the various humor types of task b, each type is divided into 4 categories: not, slightly, mildly and very. The task uses macro F1 as the evaluation standard for sub-task a, and average macro F1 as the evaluation standard for sub-tasks b and c. There are three challenges for this task. The first, how to fuse picture and text features, because whether it is a separate text or picture, the meaning expressed is lacking, and it is necessary to fuse the picture features and text semantics to obtain the best classification results. The second, the distribution of train data is imbalanced, which will increase the learn difficulty of the model. At last, the text information provided by the training data contains too much noise, which affects the semantic understanding of the text by the model and it will need to clean the data. Because we didn’t find a proper way to fuse text and picture feature, we used BERT …

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