  In recent years, long short-term memory (LSTM) has been successfully used to
model sequential data of variable length. However, LSTM can still experience
difficulty in capturing long-term dependencies. In this work, we tried to
alleviate this problem by introducing a dynamic skip connection, which can
learn to directly connect two dependent words. Since there is no dependency
information in the training data, we propose a novel reinforcement
learning-based method to model the dependency relationship and connect
dependent words. The proposed model computes the recurrent transition functions
based on the skip connections, which provides a dynamic skipping advantage over
RNNs that always tackle entire sentences sequentially. Our experimental results
on three natural language processing tasks demonstrate that the proposed method
can achieve better performance than existing methods. In the number prediction
experiment, the proposed model outperformed LSTM with respect to accuracy by
nearly 20%.
