  Consider a natural language sentence describing a specific step in a food
recipe. In such instructions, recognizing actions (such as press, bake, etc.)
and the resulting changes in the state of the ingredients (shape molded,
custard cooked, temperature hot, etc.) is a challenging task. One way to cope
with this challenge is to explicitly model a simulator module that applies
actions to entities and predicts the resulting outcome (Bosselut et al. 2018).
However, such a model can be unnecessarily complex. In this paper, we propose a
simplified neural network model that separates action recognition and state
change prediction, while coupling the two through a novel loss function. This
allows learning to indirectly influence each other. Our model, although
simpler, achieves higher state change prediction performance (67% average
accuracy for ours vs. 55% in (Bosselut et al. 2018)) and takes fewer samples to
train (10K ours vs. 65K+ by (Bosselut et al. 2018)).
