which is described below. It then encodes the textual input using an LSTM unit [@hochreiter1997long]. Finally, an attention-copy equipped decoder is used to decode the question.

More specifically, given the input sentence $S$ (containing an answer span) and the preceding context $C$, we first run a coreference resolution system to get the coref-clusters for $S$ and $C$ and use them to create a [*coreference transformed*]{} input sentence: for each pronoun, we append its most representative non-pronominal coreferent mention. Specifically, we apply the simple feedforward network based mention-ranking model of   to the concatenation of $C$ and $S$ to get the coref-clusters for all entities in $C$ and $S$. The C&M model produces a score/representation $s$ for each mention pair $(m_1,m_2)$, $$\label{equ:score}
    s(m_1, m_2) = \mathbf{W}_m h_m(m_1, m_2) + b_m$$ where $\mathbf{W}_m$ is a $1 \times d$ weight matrix and b is the bias. $h_m(m_1,m_2)$ is representation of the last hidden layer of the three layer feedforward neural network.

For each pronoun in $S$, we then heuristically identify the most “representative” antecedent from its coref-cluster. (Proper nouns are preferred.) We append the new mention after the pronoun. For example, in Table \[tab:fea\_example\], “the panthers” is the most representative mention in the coref-cluster 