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.

在自然语言理解领域中，神经模型和图形意义表达（GMR）的交叉点仍然是一个引人注目的研究领域。本文通过引入 DAGNN-plus，一种简单且参数高效的神经架构，将上下文表示学习与结构信息传播解耦，从而填补 GMR 的确切影响的空白。通过与各种序列编码器和 GMRs 配对，该架构为英文和中文的两个数据集上进行系统实验提供了基础。实证分析使用了四种不同的图形形式和九个解析器。结果对 GMRs 的理解提供了细致入微的认识，显示出四个数据集中有三个的改进，特别是依靠高度准确的解析器的英文比中文更受青睐。有趣的是，与一般领域数据集相比，GMRs 对文学领域数据集的有效性较低。这些发现为更加明智地设计 GMRs 和解析器以提高关系分类奠定了基础，预计对自然语言理解研究的未来发展产生实质性影响。