CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT
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.
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Methods
1x1 Convolution •
Adam •
Attention Dropout •
Average Pooling •
Batch Normalization •
BERT •
Bottleneck Residual Block •
Convolution •
Dense Connections •
Dropout •
GELU •
Global Average Pooling •
Kaiming Initialization •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Max Pooling •
Multi-Head Attention •
RandAugment •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Scaled Dot-Product Attention •
Softmax •
Weight Decay •
WordPiece
