RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot
  Document-Level Relation Extraction
Shiao Meng Xuming Hu Aiwei Liu Shu'ang Li Fukun Ma Yawen Yang Lijie Wen
How to identify semantic relations among entities in a document when only a
few labeled documents are available? Few-shot document-level relation
extraction (FSDLRE) is crucial for addressing the pervasive data scarcity
problem in real-world scenarios. Metric-based meta-learning is an effective
framework widely adopted for FSDLRE, which constructs class prototypes for
classification. However, existing works often struggle to obtain class
prototypes with accurate relational semantics: 1) To build prototype for a
target relation type, they aggregate the representations of all entity pairs
holding that relation, while these entity pairs may also hold other relations,
thus disturbing the prototype. 2) They use a set of generic NOTA
(none-of-the-above) prototypes across all tasks, neglecting that the NOTA
semantics differs in tasks with different target relation types. In this paper,
we propose a relation-aware prototype learning method for FSDLRE to strengthen
the relational semantics of prototype representations. By judiciously
leveraging the relation descriptions and realistic NOTA instances as guidance,
our method effectively refines the relation prototypes and generates
task-specific NOTA prototypes. Extensive experiments demonstrate that our
method outperforms state-of-the-art approaches by average 2.61% $F_1$ across
various settings of two FSDLRE benchmarks.