Previous proposals for incorporating syntactic information include the use of low-rank tensor factorizations (Lei et al., 2015), convolu-tional and time-domain neural networks (Foland and Martin, 2015), jointly embedded arguments and semantic roles in a shared vector space (FitzGerald et al., 2015), learning representations of shortest dependency paths between a predicate and its potential arguments (Roth and Lapata, 2016), encoding sentences with graph convolutional networks , constrained decoding (He et al., 2017), and argument pruning (He et al., 2018).
to these approaches, we do not use an external dependency parser but rather incorporate syntactic information as part of the model's learning objective.