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This paper presents DWIE, the 'Deutsche Welle corpus for InformationExtraction', a newly created multi-task dataset that combines four mainInformation 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 describesinteractions and properties of conceptual entities on the level of the completedocument. This contrasts with currently dominant mention-driven approaches thatstart from the detection and classification of named entity mentions inindividual sentences. Further, DWIE presented two main challenges when buildingand evaluating IE models for it. First, the use of traditional mention-levelevaluation metrics for NER and RE tasks on entity-centric DWIE dataset canresult in measurements dominated by predictions on more frequently mentionedentities. We tackle this issue by proposing a new entity-driven metric thattakes into account the number of mentions that compose each of the predictedand ground truth entities. Second, the document-level multi-task annotationsrequire the models to transfer information between entity mentions located indifferent parts of the document, as well as between different tasks, in a jointlearning setting. To realize this, we propose to use graph-based neural messagepassing techniques between document-level mention spans. Our experiments showan improvement of up to 5.5 F1 percentage points when incorporating neuralgraph propagation into our joint model. This demonstrates DWIE's potential tostimulate further research in graph neural networks for representation learningin multi-task IE. We make DWIE publicly available athttps://github.com/klimzaporojets/DWIE.

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