GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction

TL;DR

GRAPHCACHE introduces a graph neural network module that propagates dataset-level features across sentences to enhance sentence-level relation extraction, improving performance by leveraging global prior knowledge.

Contribution

The paper proposes GRAPHCACHE, a novel dataset-level feature propagation method using message passing, to enrich sentence representations for relation extraction.

Findings

Significant performance improvements on RE tasks.

Effective global feature aggregation across datasets.

Efficient online updating of property representations.

Abstract

Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a single sentence. In contrast, we model these properties from the whole dataset and use the dataset-level information to enrich the semantics of every instance. We propose the GRAPHCACHE (Graph Neural Network as Caching) module, that propagates the features across sentences to learn better representations for RE. GRAPHCACHE aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences. The global property features act as dataset-level prior knowledge for RE, and a complement to the sentence-level features. Inspired by the classical caching technique in computer systems, we develop GRAPHCACHE to update the property representations in an online manner. Overall, GRAPHCACHE yields significant effectiveness gains on RE and enables efficient message passing across all sentences in the dataset.