Rethinking Relation Classification with Graph Meaning Representations
This work addresses a gap in natural language understanding for researchers by providing systematic insights into GMRs, though it is incremental as it builds on existing methods with new experiments.
The paper tackled the problem of understanding the influence of graph meaning representations (GMRs) on relation classification tasks by introducing DAGNN-plus, a parameter-efficient neural architecture, and found that GMRs improved performance in three out of four datasets, with better results in English due to accurate parsers.
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.