Balancing signals for semi-supervised sequence learning
Ge Xu · 2019
Recurrent Neural Networks(RNNs) are powerful models that have obtained outstanding achievements in many sequence learning tasks. Despite their accomplishments, RNN models still suffer with long sequences during training. It is because error propagate backwards from output to input layers carrying gradient signals, and with long input sequence, issues like vanishing and exploding gradients can arise. This thesis reviews many current studies and existing architectures designed to circumvent the long-term dependency problems in backpropagation through time (BPTT). Mainly, we focus on the method proposed by Trinh et al. (2018) which uses semi- supervised learning method to alleviate the long-term dependency problems in BPTT. Despite the good results Trinh et al. (2018)’s model achieved, we suggest that the model can be further improved with a more systematic way of balancing auxiliary signals. In this thesis, we present our paper – RNNs with Private and Shared Representations for Semi-Supervised Learning – which is currently under review for AAAI-2019. We propose a semi-supervised RNN architecture with explicitly designed private and shared representations that regulates the gradient flow from auxiliary task to main task.