with the type that best describe the entity^\[note:notable\_type\]^. The KB copy needs subject names as the copy source, and we map entities with their names similar to those in . The data details are in Appendix A and submitted Supplementary Data.

### Evaluation Metrics {#subsubsec:metrics}

Following [@P16-1056; @N18-1020], we adopt some word-overlap based metrics (WBMs) for natural language generation including BLEU-4 [@Papineni:2002:BMA:1073083.1073135], ROUGE$_{\rm L}$ [@Lin:2004] and METEOR [@W14-3348]. However, such metrics still suffer from some limitations [@novikova-EtAl:2017:EMNLP2017]. Crucially, it might be difficult for them to measure whether generated questions that express the given predicate and refer to definitive answers. To better evaluate generated questions, we run two further evaluations as follows. (1) ***Predicate identification***: Following , we employ annotators to judge whether the generated question expresses the given predicate in the fact or not. The score for predicate identification is the percentage of generated questions that express the given predicate. (2) ***Answer coverage***: We define a novel metric called answer coverage to identify whether the generated question refers to a definitive answer. Specifically, answer coverage is obtained by automatically calculating the percentage of questions that contain answer type words, and answer type words are object contexts (entity types for the 