Markus Eberts


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An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
Markus Eberts | Adrian Ulges
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We present a joint model for entity-level relation extraction from documents. In contrast to other approaches - which focus on local intra-sentence mention pairs and thus require annotations on mention level - our model operates on entity level. To do so, a multi-task approach is followed that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. We achieve state-of-the-art relation extraction results on the DocRED dataset and report the first entity-level end-to-end relation extraction results for future reference. Finally, our experimental results suggest that a joint approach is on par with task-specific learning, though more efficient due to shared parameters and training steps.


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ManyEnt: A Dataset for Few-shot Entity Typing
Markus Eberts | Kevin Pech | Adrian Ulges
Proceedings of the 28th International Conference on Computational Linguistics

We introduce ManyEnt, a benchmark for entity typing models in few-shot scenarios. ManyEnt offers a rich typeset, with a fine-grain variant featuring 256 entity types and a coarse-grain one with 53 entity types. Both versions have been derived from the Wikidata knowledge graph in a semi-automatic fashion. We also report results for two baselines using BERT, reaching up to 70.68% accuracy (10-way 1-shot).