An illustrative example of the GAN structure in sequence labeling scenario (argument role labeling scenario has the identical frameworks except vector dimensions). As introduced in Section 5, the “real data” in the original GAN is replaced by feature/state representation (Equation (1), or Equation (6) for argument role labeling scenario) and ground-truth labels (expert actions) in our framework, while the “generator data” consists of features and extractor's attempt labels (agent actions). The discriminator serves as the reward estimator and a linear transform is utilized to extend the D's original output of probability range [0, 1].