Extracting Action Sequences from Texts Based on Deep Reinforcement Learning
Table 2: F1 scores of different methods in extracting all types of action names and all types of action arguments
['Method', '[BOLD] Action Names  [BOLD] WHS', '[BOLD] Action Names  [BOLD] CT', '[BOLD] Action Names  [BOLD] WHG', '[BOLD] Action Arguments  [BOLD] WHS', '[BOLD] Action Arguments  [BOLD] CT', '[BOLD] Action Arguments  [BOLD] WHG']
We set the restriction n=2 and n=8 for EAD, CMLP and BLCC which need one-to-one sequence pairs, and no restriction for STFC and EASDRL. In all of our datasets, the arguments of an action are either all essential arguments or one exclusive argument pair together with all other essential arguments, which means at most 21 sequences can be generated. Therefore, the results of action arguments extraction are identical when n=2 and n=8. We observe that the number of arguments in most texts of the CT dataset is very small, such that BLCC performs well on extracting arguments in the CT dataset. On the other hand, we can also observe that BLCC, EAD and CMLP get worse performance when relaxing the restriction on n (n=2 and n=8). The reason is that when given a single text with many possible output sequences, these models learn common parts (essential items) of outputs, neglecting the different parts (optional or exclusive items). We can also see that both sequence labeling method and encoder-decoder structure do not work well, which exhibits that, in this task, our reinforcement learning framework can indeed perform better than traditional methods.