Capturing interactions among multiple predicate-argument structures (PASs) is
a crucial issue in the task of analyzing PAS in Japanese. In this paper, we
propose new Japanese PAS analysis models that integrate the label prediction
information of arguments in multiple PASs by extending the input and last
layers of a standard deep bidirectional recurrent neural network (bi-RNN)
model. In these models, using the mechanisms of pooling and attention, we aim
to directly capture the potential interactions among multiple PASs, without
being disturbed by the word order and distance. Our experiments show that the
proposed models improve the prediction accuracy specifically for cases where
the predicate and argument are in an indirect dependency relation and achieve a
new state of the art in the overall $F_1$ on a standard benchmark corpus.