  In the field of natural language understanding, the intersection of neural
models and graph meaning representations (GMRs) remains a compelling area of
research. Despite the growing interest, a critical gap persists in
understanding the exact influence of GMRs, particularly concerning relation
extraction tasks. Addressing this, we introduce DAGNN-plus, a simple and
parameter-efficient neural architecture designed to decouple contextual
representation learning from structural information propagation. Coupled with
various sequence encoders and GMRs, this architecture provides a foundation for
systematic experimentation on two English and two Chinese datasets. Our
empirical analysis utilizes four different graph formalisms and nine parsers.
The results yield a nuanced understanding of GMRs, showing improvements in
three out of the four datasets, particularly favoring English over Chinese due
to highly accurate parsers. Interestingly, GMRs appear less effective in
literary-domain datasets compared to general-domain datasets. These findings
lay the groundwork for better-informed design of GMRs and parsers to improve
relation classification, which is expected to tangibly impact the future
trajectory of natural language understanding research.
