type “administrative region" rather than a refined type “US state" for the entity “*New York*"). Therefore, generated questions from may be difficult to contain definitive answers.

To address the aforementioned two issues, we exploit more **diversified contexts** for the given facts as textual contexts in an encoder-decoder model. Specifically, besides using predicate contexts from the distant supervision utilized by , we further leverage the domain, range and even topic for the given predicate as contexts, which are off-the-shelf in KBs (e.g. the range and the topic for the predicate `fb:location/containedby` are “location" and “containedby", respectively^\[omit\_domain\]^). Therefore, 100% predicates (rather than 44%^\[predicate\ context\ percentage\]^ of those in @N18-1020) have contexts. Furthermore, in addition to the most frequently mentioned entity type as contexts used by , we leverage the type that best describes the entity as contexts (e.g. a refined entity type[^3] “US state" combines a broad type “administrative region" for the entity “New York"), which is helpful to refine the entity information. Finally, in order to make full use of these contexts, we propose context-augmented fact encoder and multi-level copy mechanism (KB copy and context copy) to integrate diversified contexts, where the multi-level copy mechanism can copy from KB and textual 