GADePo: Graph-Assisted Declarative Pooling Transformers for
  Document-Level Relation Extraction
Andrei C. Coman Christos Theodoropoulos Marie-Francine Moens James Henderson
Document-level relation extraction aims to identify relationships between
entities within a document. Current methods rely on text-based encoders and
employ various hand-coded pooling heuristics to aggregate information from
entity mentions and associated contexts. In this paper, we replace these rigid
pooling functions with explicit graph relations by leveraging the intrinsic
graph processing capabilities of the Transformer model. We propose a joint
text-graph Transformer model, and a graph-assisted declarative pooling (GADePo)
specification of the input which provides explicit and high-level instructions
for information aggregation. This allows the pooling process to be guided by
domain-specific knowledge or desired outcomes but still learned by the
Transformer, leading to more flexible and customizable pooling strategies. We
extensively evaluate our method across diverse datasets and models, and show
that our approach yields promising results that are comparable to those
achieved by the hand-coded pooling functions.