negative labeled F1, for trees), we use the risk objective: which measures the model's expected cost over possible outputs y for each of the training exam- Minimizing a risk objective has a long history in structured prediction (Povey and Woodland, 2002;Smith and Eisner, 2006;Li and Eisner, 2009;Gimpel and Smith, 2010) but often relies on the cost function decomposing according to the output structure.
we can avoid any restrictions on the cost using reinforcement learning-style approaches (Xu et al., 2016;Shen et al., 2016;Edunov et al., 2017) where cost is ascribed to the entire output structure -albeit at the expense of introducing a potentially difficult credit assignment problem.