This is the official implementation of our AAAI-21 accepted paper Label Confusion Learning to Enhance Text Classification Models.

The structure of LCM looks like this:

Here we provide some demo experimental code & datasets.


python 3.6 tensorflow 2.2.0 keras 2.3.1

Run a Demo:

LCM-based LSTM:

Run python to compare the performance of LSTM, LSTM with label smoothing(LS) and LSTM with LCM.

LCM-based BERT:

Run python to compare the performance of BERT, BERT with label smoothing(LS) and BERT with LCM.

The final results will be outputted to output/ directory.

The curve below shows our results on 20NG with LSTM as basic predictor. By changing the α, we can control the influence of LCM on the original model.