As shown in Chen and Ng (2016), traditional hand-crafted features are crucial for the resolver's success since they capture the syntactic, positional and other relationships between an AZP and its candidate antecedents.
to evaluate the importance of each candidate antecedent in a comprehensive manner, following Chen and Ng (2016) who encode hand-crafted features as inputs to their network, we integrate a set of features that are utilized in Chen and Ng (2016), in the form of vector (v (f eature) ) into our attention model.