DWIE: an entity-centric dataset for multi-task document-level information extraction

TL;DR

DWIE is a comprehensive, entity-centric dataset for multi-task document-level information extraction that introduces new evaluation metrics and leverages graph neural networks to improve model performance.

Contribution

The paper introduces DWIE, a novel entity-centric dataset for multi-task IE, and proposes new metrics and graph-based methods to enhance model training and evaluation.

Findings

Up to 5.5 F1 improvement with graph neural message passing.

Entity-centric evaluation metrics better reflect model performance.

DWIE dataset facilitates research in multi-task, document-level IE.

Abstract

This paper presents DWIE, the 'Deutsche Welle corpus for Information Extraction', a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start from the detection and classification of named entity mentions in individual sentences. Further, DWIE presented two main challenges when building and evaluating IE models for it. First, the use of traditional mention-level evaluation metrics for NER and RE tasks on entity-centric DWIE dataset can result in measurements dominated by predictions on more frequently mentioned entities. We tackle this issue by proposing a new entity-driven metric that takes into account the number of mentions that compose each of the predicted and ground truth entities. Second, the document-level multi-task annotations require the models to transfer information between entity mentions located in different parts of the document, as well as between different tasks, in a joint learning setting. To realize this, we propose to use graph-based neural message passing techniques between document-level mention spans. Our experiments show an improvement of up to 5.5 F1 percentage points when incorporating neural graph propagation into our joint model. This demonstrates DWIE's potential to stimulate further research in graph neural networks for representation learning in multi-task IE. We make DWIE publicly available at https://github.com/klimzaporojets/DWIE.