object is the correct answer. Therefore, it is a key issue for KBQG to correctly understand the knowledge symbols (subject, predicate and object in the triplet fact) and then generate corresponding text descriptions. More recently, some researches have striven toward this task, where the behind intuition is to construct implicit associations between facts and texts. Specifically, designed an encoder-decoder architecture to generate questions from structured triplet facts. In order to improve the generalization for KBQG, utilized extra contexts as input via distant supervisions [@Mintz:2009:DSR:1690219.1690287], then a decoder is equipped with attention and part-of-speech (POS) copy mechanism to generate questions. Finally, this model obtained significant improvements. Nevertheless, we observe that there are still two important research issues (RIs) which are not processed well or even neglected.

***RI-1:** The generated question is required to **express the given predicate** in the fact*. For example in Figure \[fig:example\], Q1 does not express (match) the predicate (`fb:location/containedby`) while it is expressed in Q2 and Q3. Previous work [@N18-1020] usually obtained predicate textual contexts through distant supervision. However, the distant supervision is noisy or even wrong (e.g. “X is the husband of Y" is the relational pattern for the predicate `fb:marriage/spouse`, so it is wrong when 