Object-oriented Neural Programming (OONP) for Document Understanding
Table 2: The performance a implementation of OONP on bAbI task 2.
['model', 'F1 (for links) %', 'RandIndex (for objects)%']
, we use 1,000 episodes with length evenly distributed from one to six. We use just REINFORCE with only the final reward defined as the overlap between the generated ontology and the ground truth, while step-by-step supervision on actions yields almost perfect result (result omitted). with results summarized in OONP can learn fairly well on recovering the evolving ontology with such a small training set and weak supervision (RL with the final reward), which clearly shows that the credit assignment over to earlier snapshots does not cause much difficulty in the learning of OONP even with a generic policy search algorithm. It is not so surprising to observe that Symbolic Reasoner helps to improve the results on discovering the links, while it does not improves the performance on identifying the objects although it is taken within the learning. It is quite interesting to observe that OONP achieves rather high accuracy on discovering the links while it performs relatively poorly on specifying the objects. It is probably due to the fact that the rewards does not penalizes the objects.