  Japanese predicate-argument structure (PAS) analysis involves zero anaphora
resolution, which is notoriously difficult. To improve the performance of
Japanese PAS analysis, it is straightforward to increase the size of corpora
annotated with PAS. However, since it is prohibitively expensive, it is
promising to take advantage of a large amount of raw corpora. In this paper, we
propose a novel Japanese PAS analysis model based on semi-supervised
adversarial training with a raw corpus. In our experiments, our model
outperforms existing state-of-the-art models for Japanese PAS analysis.
