[@P16-1154], we directly copy words in the textual contexts $C$, and it does not rely on any POS tagging. Specifically, the input sequence $\chi$ for the context copy is the concatenation of all words in the textual contexts $C$. Unfortunately, $\chi$ is prone to contain repeated words because it consists of rich contexts for subject, predicate and object. The repeated words in the input sequence tend to cause repetition problems in output sequences [@P16-1008]. We adopt the maxout pointer [@D18-1424] to address the repetition problem. Instead of summing all the probabilistic scores for repeated input words, we limit the probabilistic score of repeated words to their maximum score as Equation \[eq:cpctx\]:$$\begin{split}
P_{cpctx}(y_t)=\left\{\begin{matrix}
\underset{m,\, where\, \chi_{m}=y_{t}}{{\rm max}}\, sc_{t,m} \quad \quad y_{t} \in \chi \\ 
~~~~~~~~~~0 ~~~~~~~~~~~~~~~~~~~~~ otherwise
\end{matrix}\right.
\end{split}
\label{eq:cpctx}$$ $$\begin{split}
P_{cpctx}(y_t|.)\!\!=\!\!\left\{\!\begin{matrix}
\underset{m,\, where\, \chi_{m}=y_{t}}{{\rm max}} sc_{t,m} \quad  y_{t} \! \in \! \chi \\ 
~~~~~~~~~~0 ~~~~~~~~~~~~~~~ otherwise
\end{matrix}\right. 
\end{split}
\label{eq:cpctx}$$ where $\chi_{m}$ represents the $m$-th token in the input context sequence $\chi$, $sc_{t,m}$ is the probabilistic score of generating the token $\chi_m$ at time step $t$, and $sc_{t,m}$ is calculated by a softmax function over $\chi$.

Learning
--------

### Question-Aware Loss {#subsubsec:ques_loss}

It is totally differential for our model to obtain 