  With the advent of FrameNet and PropBank, many semantic role labeling (SRL)
systems have been proposed in English. Although research on Japanese predicate
argument structure analysis (PASA) has been conducted, most studies focused on
surface cases. There are only few previous works on Japanese SRL for deep
cases, and their models' accuracies are low. Therefore, we propose a
hierarchical multitask learning method with dependency parsing (DP) and show
that our model achieves state-of-the-art results in Japanese SRL. Also, we
conduct experiments with a joint model that performs both argument
identification and argument classification simultaneously. The result suggests
that multitasking with DP is mainly effective for argument identification.
