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Japanese predicate-argument structure (PAS) analysis involves zero anaphoraresolution, which is notoriously difficult. To improve the performance ofJapanese PAS analysis, it is straightforward to increase the size of corporaannotated with PAS. However, since it is prohibitively expensive, it ispromising to take advantage of a large amount of raw corpora. In this paper, wepropose a novel Japanese PAS analysis model based on semi-supervisedadversarial training with a raw corpus. In our experiments, our modeloutperforms existing state-of-the-art models for Japanese PAS analysis.

Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis