Augmenting Document-level Relation Extraction with Efficient Multi-Supervision

TL;DR

This paper introduces an efficient multi-supervision approach for document-level relation extraction that combines expert and distant supervision to select informative data and uses a ranking loss to improve robustness and performance.

Contribution

It proposes a novel method that enhances document-level relation extraction by selecting informative data and integrating multiple supervision sources with a ranking loss.

Findings

Improves model performance over baselines.

Increases time efficiency in training.

Effectively reduces noise impact.

Abstract

Despite its popularity in sentence-level relation extraction, distantly supervised data is rarely utilized by existing work in document-level relation extraction due to its noisy nature and low information density. Among its current applications, distantly supervised data is mostly used as a whole for pertaining, which is of low time efficiency. To fill in the gap of efficient and robust utilization of distantly supervised training data, we propose Efficient Multi-Supervision for document-level relation extraction, in which we first select a subset of informative documents from the massive dataset by combining distant supervision with expert supervision, then train the model with Multi-Supervision Ranking Loss that integrates the knowledge from multiple sources of supervision to alleviate the effects of noise. The experiments demonstrate the effectiveness of our method in improving the model performance with higher time efficiency than existing baselines.