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Thank you for the good words. I am happy to understand that our work is appreciated.
For Task 1, I am thinking about the following things, that could lead to better performing model.
We didn’t played enough with the hypeparameters and the architecture of the neural network.
For example, the text is padded to the mean number of tokens in article, which means that for half of the articles, some information is dropped.
If the padding parameter is adjusted, we can feed more information to the neural network.
On the other hand, the LSTMs are not working very well with long sequences, so experimenting with different layers here could be beneficial.
For example, an attention mechanism added to the LSTM layer could be tested here.
Using different types of text representations, models and feature engineering, should explain and catch different connections in the texts.
Similar to the approach we have in Task 2, creating a stacked ensemble should give a better performing final model.