Papers by Markus Eberts
ManyEnt: A Dataset for Few-shot Entity Typing (2020.coling-main)
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| Challenge: | ManyEnt is a benchmark for entity typing in few-shot scenarios . given a sentence with two entity mentions, the model is to assign the entity pair to a relation. |
| Approach: | They propose a benchmark for entity typing models in few-shot scenarios . they propose 256 entity types and 53 entity types for a fine-grain variant . |
| Outcome: | The proposed model can be used in few-shot scenarios with a fine-grain typeset and a coarse-grainability one with 53 entity types. |
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning (2021.eacl-main)
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| Challenge: | Using a multi-task approach, we extract facts from documents at entity level. |
| Approach: | They propose a multi-task approach that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. |
| Outcome: | The proposed model is on par with task-specific learning, though more efficient due to shared parameters and training steps. |