[@W18-2601] to integrate the context matrix and the fact embedding. For example, the subject context matrix $\textbf{C}^{s}=\{\textbf{c}_{1}^{s},\textbf{c}_{2}^{s},...,\textbf{c}_{|s|}^{s}\}$ and the subject embedding vector $\textbf{e}^{s}$ are integrated by the following gated fusion: $$\begin{gathered}
    \textbf{f} = {\rm tanh}(\textbf{W}_f[\textbf{c}^{s},\textbf{e}^{s}]) \label{eq:f} \\
    \textbf{g} = {\rm sigmoid}(\textbf{W}_g[\textbf{c}^{s},\textbf{e}^{s}]) \label{eq:g} \\
    \textbf{h}^{s} = \textbf{g} \ \odot \ \textbf{f} + (1-\textbf{g})  \ \odot \ \textbf{e}^{s} \label{eq:fusion}\end{gathered}$$where $\textbf{c}^{s}$ is an attentive vector from $\textbf{e}^{s}$ to $\textbf{C}^{s}$, which is similar to . The attentive vector $\textbf{c}^{s}$ is combined with original subject embedding $\textbf{e}^{s}$ as a new enhanced representation $\textbf{f}$ (Equation \[eq:f\]). And then a learnable gate vector, $\textbf{g}$ (Equation \[eq:g\]), controls the information from $\textbf{c}^{s}$ and $\textbf{e}^{s}$ to the final augmented subject vector $\textbf{h}^{s} \in \mathbb{R} ^{d}$ (Equation \[eq:fusion\]), where $\odot$ denotes the element-wise multiplication. Similarly, the augmented predicate vector $\textbf{h}^{p}$ and the augmented object vector $\textbf{h}^{o}$ are calculated in the same way. Finally, the context-augmented fact representation $\textbf{H}_{f} \in \mathbb{R} ^{3,d}$ is the concatenation of augmented vectors as follows:$$\begin{split}
\textbf{H}_{f} = [\textbf{h}^{s}; \textbf{h}^{p}; \textbf{h}^{o}]
\end{split}
\label{eq:3411}$$

Decoder {#subsec:dec}
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The decoder aims at generating a question word sequence. As shown in Figure \[fig:overall-structure\], we also exploit the transformer as the basic block in our decoder. Then we use a multi-level 