C)
\end{split}
\label{eq:3411}$$ where $F=(s,p,o)$ represents the subject ($s$), predicate ($p$) and object ($o$) of the input triplet, $C=\{\textbf{x}^{s}, \textbf{x}^{p}, \textbf{x}^{o}\}$ denotes a set of additional textual contexts, $Y=(y_1,y_2,...,y_{|Y|})$ is the generated question, $y_{<t}$ represents all previously generated question words before time step $t$.

Methodology
===========

Our model extends the encoder-decoder architecture [@cho-EtAl:2014:EMNLP2014] with three encoding modules and two copy mechanisms in the decoder. The model overview is shown in Figure \[fig:overall-structure\] along with its caption. It should be emphasized that we additionally design an answer-aware loss to make the generated question associated with a definitive answer (Sec. \[subsubsec:ans\_loss\]).

Context Encoder {#subsec:ctx_enc}
---------------

Inspired by the great success of transformer [@NIPS2017_7181] in sequence modeling [@shen2018disan], we adopt a transformer encoder to encode each textual context separately. Take the subject context $\textbf{x}^{s}$ as an example, $\textbf{x}^{s}=(x_{1}^{s}, x_{2}^{s},...,x_{|s|}^{s})$ is concatenated from diversified types for the subject, and $x_{i}^{s}$ is the $i$-th token in the subject context, $|s|$ stands for the length of the subject context. Firstly, $\textbf{x}^{s}$ is mapped into a query matrix $\textbf{Q}$, where $\textbf{Q}$ is constructed by summing the corresponding token embeddings and segment embeddings. Similar to BERT [@cotterell-eisner-2018-deep], segment embeddings are the same for tokens of $\textbf{x}^{s}$ but different 