Previous work has shown that neural-networkbased approaches models are powerful for English SRL (He et al., 2017) as well as Japanese case-marker disambiguation and anaphora detection (Ouchi et al., 2015;Shibata et al., 2016;Ouchi et al., 2017;Matsubayashi and Inui, 2018); however, neural-network-based models have various tuning parameters such as network structure, hyper parameters, and several methods of avoiding over fitting.
it is not easy to determine the cause-and-effect relations between models and the final results.