\vspace{-0.1cm}
\end{gathered}$$ where $\odot$ denotes an element-wise product between two vectors and ReLU is the rectified linear activation function. $score_i$ denotes the mention-pair score for each antecedent token (e.g., “the” and “panthers”) with the pronoun (e.g., “they”); $score_i$ is obtained from the trained model (Equation \[equ:score\]) of the C&M. If token $i$ is not added later as an antecedent token, $score_i$ is set to zero. $\mathbf{W}_a$, $\mathbf{W}_b$ are weight matrices and $b$ is the bias vector.

[**Answer Feature Embedding**]{} We also include an answer position feature embedding to generate answer-specific questions; we denote the answer span with the usual BIO tagging scheme (see, e.g., “the arizona cardinals” in Table \[tab:fea\_example\]). During training and testing, the answer span feature (i.e., “`B_ANS`”, “`I_ANS`” or “`O`”) is mapped to its feature embedding space: $\mathbf{f^a} = (a_1, ..., a_n)$.

[**Word Embedding**]{} To obtain the word embedding for the tokens themselves, we just map the tokens to the word embedding space: $\mathbf{x} = (x_1,...,x_n)$.

[**Final Encoder Input**]{} As noted above, the final input to the LSTM-based encoder is a concatenation of (1) the refined coreference position feature embedding (light blue units in Figure \[fig:nn\]), (2) the answer position feature embedding (red units), and (3) the 