for “they”. The new sentence with the appended coreferent mention is our [*coreference transformed*]{} input sentence $S^{'}$ (see Figure \[fig:nn\]).

[**Coreference Position Feature Embedding**]{} For each token in $S^{'}$, we also maintain one position feature $\mathbf{f^c} = (c_1,...,c_n)$, to denote pronouns (e.g., “they”) and antecedents (e.g., “the panthers”). We use the BIO tagging scheme to label the associated spans in $S^{'}$. “`B_ANT`” denotes the start of an antecedent span, tag “`I_ANT`” continues the antecedent span and tag “`O`” marks tokens that do not form part of a mention span. Similarly, tags “`B_PRO`” and “`I_PRO`” denote the pronoun span. (See Table \[tab:fea\_example\], “coref. feature”.)

[**Refined Coref. Position Feature Embedding**]{} Inspired by the success of gating mechanisms for controlling information flow in neural networks [@hochreiter1997long; @dauphin2017language], we propose to use a gating network here to obtain a refined representation of the coreference position feature vectors $\mathbf{f^c} = (c_1,...,c_n)$. The main idea is to utilize the mention-pair score (see Equation \[equ:score\]) to help the neural network learn the importance of the coreferent phrases. We compute the refined (gated) coreference position feature vector $\mathbf{f^d} = (d_1,...,d_n)$ as follows, $$\begin{gathered}
\label{equ:gate}
g_i = \textnormal{ReLU}(\mathbf{W}_a c_i + \mathbf{W}_b score_i + b)\\
d_i = g_i \odot c_i
