Hey, I am working on Binary Label Text Classification problem for fake news detection. I have done some digging and found that Two Level Convolutional Neural Network (TCNN) have interesting result. But is there any state-of-the-art algorithm/model which can help me in this task?
A variant of RNN called Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (Bi-LSTM) usually create good results. Ensemble models those combine Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory also may produce good results.
A variant of RNN called Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (Bi-LSTM) usually create good results. Ensemble models those combine Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory also may produce good results.
A great website to find the SOTA machine learning Papers and Code is https://paperswithcode.com/
BERT is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks.
The research paper from Google that proposes BERT is found below
https://arxiv.org/pdf/1810.04805.pdf
BERT python code (kaggle fake news challenge)
https://www.kaggle.com/bbqlp33/bert-huggingface-pytorch-pretrained-bert
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