Instead of using an explicit graphical model, we enforce a relational bias on model parameters, which does not introduce extra burden on label decoding.
the task we consider takes a raw sentence C as well as an entity mention span M inside C as inputs, and aims to predict the correct type labels T m of M from a candidate type set T , which includes more than 10,000 free-form types.