Question Answering from Unstructured Text by Retrieval and Comprehension
Table 8: Ratio of the gate being open. (g>0.5) If the answer is named entity, the model need to select answer from text. Therefore, g=0. Bold font indicates winning model. Vocabulary Only model wins when g is high.
['[BOLD] Question Type', '[BOLD] ratio', '[BOLD] r1+A', '[BOLD] r1+V']
The benefit of the mixture model comes from the fact that ppointer works well for some question types, while pvocab works well for others. For question types “Movie to Language” and “Movie to Genre” (the so called “choice questions”) the number of possible answers is small. For this case, even if the answer can be found in the context, it is easier for the model to select answer from an external vocabulary which encodes global statistics about the entities. For other “free questions”, depending on the question type, one approach is better than the other. Our model is able to successfully estimate the latent category and switch the model type by controlling the coefficient g.