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

arXiv: https://arxiv.org/abs/1806.00971 · HTML: https://mlsanity.com/paper/1806.00971/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution](https://mlsanity.com/paper/2104.07425/) (2021)

## Abstract (arXiv)

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.