Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features
Table 7: Amount of unique questions generated.
['Model', 'Unique Q’s']
Question Diversity. We are interested in how the new features effect the quantity of unique questions produced by our model. Therefore, we counted the number of unique questions output by the model when considering the entire testing set as inputs. Here, we measure similarity using a strict character match comparison. We can see that the FocusCR model produces 10,194 unique questions, which is a 55% increase over the 6,595 unique questions produced by the Baseline model. Although strict character matching is a crude method of measuring question similarity, we conclude that the features incorporated into the FocusCR model have a positive effect on the diversity of generated questions.