It improves the best F-score of our self-attention based models from 89.2% to 91.1%, which indicates that our novel proposed weight sharing model is capable of learning useful language knowledge from unlabelled noisy dataset automatically.
we share the weights of the last few layers of two encoders that are responsible for extracting high-level representations of input sentences.