Machine comprehension improves domain-specific japanese predicate-argument structure analysis

Abstract

To improve the accuracy of predicate- argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable. We focus on a specific domain, specifically Japanese blogs on driv- ing, and construct two wide-coverage datasets as a form of QA using crowdsourcing: A PAS-QA dataset and a reading comprehension QA (RC-QA) dataset. We train a machine comprehension (MC) model based on these datasets to perform PAS analysis. Our experi- ments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.