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Hierarchical Multitask Learning with Dependency Parsing for Japanese Semantic Role Labeling Improves Performance of Argument Identification

2021-01-15 11:41:20
Tomohiro Nakamura, Tomoya Miyashita, Soh Ohara
arXiv_CL
       
Abstract

With the advent of FrameNet and PropBank, many semantic role labeling (SRL) systems have been proposed in English. Although research on Japanese predicate argument structure analysis (PASA) has been conducted, most studies focused on surface cases. There are only few previous works on Japanese SRL for deep cases, and their models' accuracies are low. Therefore, we propose a hierarchical multitask learning method with dependency parsing (DP) and show that our model achieves state-of-the-art results in Japanese SRL. Also, we conduct experiments with a joint model that performs both argument identification and argument classification simultaneously. The result suggests that multitasking with DP is mainly effective for argument identification.

Abstract (translated)

URL

https://arxiv.org/abs/2101.06071

PDF

https://arxiv.org/pdf/2101.06071.pdf

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