  An event-noun is a noun that has an argument structure similar to a
predicate. Recent works, including those considered state-of-the-art, ignore
event-nouns or build a single model for solving both Japanese predicate
argument structure analysis (PASA) and event-noun argument structure analysis
(ENASA). However, because there are interactions between predicates and
event-nouns, it is not sufficient to target only predicates. To address this
problem, we present a multi-task learning method for PASA and ENASA. Our
multi-task models improved the performance of both tasks compared to a
single-task model by sharing knowledge from each task. Moreover, in PASA, our
models achieved state-of-the-art results in overall F1 scores on the NAIST Text
Corpus. In addition, this is the first work to employ neural networks in ENASA.
