KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes
This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes. We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively. Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.
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Methods
1x1 Convolution •
Adam •
Attention Dropout •
Average Pooling •
Batch Normalization •
BERT •
Convolution •
Dense Connections •
Depthwise Convolution •
Depthwise Separable Convolution •
DistilBERT •
Dropout •
GELU •
Inverted Residual Block •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Pointwise Convolution •
ReLU •
Residual Connection •
RMSProp •
Scaled Dot-Product Attention •
Sigmoid Activation •
Softmax •
Squeeze-and-Excitation Block •
Swish •
Weight Decay •
WordPiece
