  The goal of knowledge representation learning is to embed entities and
relations into a low-dimensional, continuous vector space. How to push a model
to its limit and obtain better results is of great significance in knowledge
graph's applications. We propose a simple and elegant method, Trans-DLR, whose
main idea is dynamic learning rate control during training. Our method achieves
remarkable improvement, compared with recent GAN-based method. Moreover, we
introduce a new negative sampling trick which corrupts not only entities, but
also relations, in different probabilities. We also develop an efficient way,
which fully utilizes multiprocessing and parallel computing, to speed up
evaluation of the model in link prediction tasks. Experiments show that our
method is effective.
