Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling

A new framework created by Massachusetts Institute of Technology(in Stanford University) and Richard Socher(in Salesforce Research)

• Goal: A new framework
• Advantage: greatly reducing the number of trainable variables.
• Experiments: Their LSTM model lowers the state of the art word-level perplexity on the Penn Treebank to 68.5.