A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling
This addresses incomplete labeling in document-level relation extraction, an incremental improvement over existing positive-unlabeled methods.
The paper tackles document-level relation extraction with incomplete labeling by proposing P3M, a positive-unlabeled metric learning framework that improves F1 scores by approximately 4-10 points and achieves state-of-the-art results in fully labeled scenarios.
The goal of document-level relation extraction (RE) is to identify relations between entities that span multiple sentences. Recently, incomplete labeling in document-level RE has received increasing attention, and some studies have used methods such as positive-unlabeled learning to tackle this issue, but there is still a lot of room for improvement. Motivated by this, we propose a positive-augmentation and positive-mixup positive-unlabeled metric learning framework (P3M). Specifically, we formulate document-level RE as a metric learning problem. We aim to pull the distance closer between entity pair embedding and their corresponding relation embedding, while pushing it farther away from the none-class relation embedding. Additionally, we adapt the positive-unlabeled learning to this loss objective. In order to improve the generalizability of the model, we use dropout to augment positive samples and propose a positive-none-class mixup method. Extensive experiments show that P3M improves the F1 score by approximately 4-10 points in document-level RE with incomplete labeling, and achieves state-of-the-art results in fully labeled scenarios. Furthermore, P3M has also demonstrated robustness to prior estimation bias in incomplete labeled scenarios.