The simplicity of our models not only implies better generalization, more robust estimation of parameters in the wake of small dataset sizes, and better interpret-ability but also yields state of the art performance.
we show that by exploiting structural constraints, blend formation can be modeled as a simple sequence labeling problem as opposed to prior work which models it as a general sequence to sequence problem.