Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
Table 4: Results of neural network models trained on the noisy labelled dataset and tested on the 2k labelled dataset. Results are separated by model architecture used (CNN, or LSTM) and sorted by the input combinations and the number of epochs used in training. Lowest MSE for each model is highlighted in bold. Text only models (using only the right sub-network of the LSTM or CNN model architectures shown above) are indicated with a − in the Vector Input column.
['Model', 'Text', 'Vector', '# epochs', 'MSE', 'RMSE', 'MAE']
Next, we compare different combinations of models trained on this noisy dataset. As before, we use a validation split of 80-20 during training, so each model is trained on approximately 80 thousand examples and validated on 20 thousand examples before being tested on the 2k labelled dataset. However, we see that these models did not outperform our previous models trained on the 20k labelled data. Because of the computation time needed to extract the 2048-dimensional image vectors for each image referenced in the unlabelled dataset and the lack of significant performance differences we saw in the previous tables, we do not include results of models including image vector representations.