  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.
