\\
    p_{genv}, p_{cpkb}, p_{cpctx} \! = \! {\rm softmax}([\textbf{s}_t, \textbf{y}_{t-1}]) \label{eq:mode}\end{gathered}$$where $genv$, $cpkb$ and $cpctx$ denote the vocab generation mode, the KB copy mode and the context copy mode, respectively. In order to control the balance among different modes, we employ a 3-dimensional switch probability in Equation \[eq:mode\], where $\textbf{y}_{t-1}$ is the embedding of previous generated word, $P_{\cdot}(\cdot | \cdot)$ indicates the probabilistic score function for generated target word of each mode. In the three probability score functions, $P_{vocab}$ is typically performed by a $softmax$ classifier over a fixed vocabulary $V$ based on the word embedding similarity, and the details of $P_{cpkb}$ and $P_{cpctx}$ are in the following.

### KB Copy

Previous study found that most questions contain the subject name or its aligns in SimpleQuestion [@D18-1051]. However, the predicate name and object name hardly appear in the question. Therefore, we only copy the subject name in the KB copy, where $P_{cpkb}(y_{t}|\textbf{s}_t,f)$, the probability of copying the subject name, is calculated by a neural network function with a multi-layer perceptron (MLP) projected from $\textbf{s}_t$.

### Context Copy

demonstrated the effectiveness of POS copy for the context. However, such a copy mechanism heavily relies on POS tagging. Inspired by the CopyNet 