object are regarded as answer type words). Furthermore, it is hard to automatically evaluate the naturalness of generated questions. Following , we adopt human evaluation to measure the naturalness by a score of 0-5.

### Comparison with State-of-the-arts

We compare our model with following methods.

\(1) *Template*: A baseline in , it randomly chooses a candidate fact $F_c$ in the training data to generate the question, where $F_c$ shares the same predicate with the input fact.

\(2) **: We compare our methods with the single placeholder model, which performs best in . (3) **: We compare our methods with the model utilizing copy actions, the best performing model in . Although this model is designed to a zero-shot setting (for unseen predicates and entity type), it has good abilities to generate better questions (on known or unknown predicates and entity types) represented in the additional context input and SPO copy mechanism.

### Implementation Details

To make our model comparable to the comparison methods, we keep most parameter values the same as . We utilize RMSProp algorithm with a decreasing learning rate (0.001), batch size (200) to optimize the model. The size of KB embeddings is 200, and KB embeddings are 