Papers by Claudia Schulz
Biomedical Concept Relatedness – A large EHR-based benchmark (2020.coling-main)
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| Challenge: | Existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. |
| Approach: | They propose to use a concept relatedness benchmark to test the suitability of AI in healthcare . they find that it is six times larger than existing concepts relatedness datasets . |
| Outcome: | The proposed benchmark is six times larger than existing biomedical concept relatedness datasets and is relevant for the application of interest. |
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains (P19-1)
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Claudia Schulz, Christian M. Meyer, Jan Kiesewetter, Michael Sailer, Elisabeth Bauer, Martin R. Fischer, Frank Fischer, Iryna Gurevych
| Challenge: | Existing deep learning methods require large amounts of training data to achieve reasonable performance. |
| Approach: | They propose to generate automatic annotation suggestions for a discourse-level sequence labelling task that requires extensive domain expertise. |
| Outcome: | The proposed model improves with newly annotated texts while introducing no biases. |
Multi-Task Learning for Argumentation Mining in Low-Resource Settings (N18-2)
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| Challenge: | Argument component identification is difficult for trained annotators to perform in a new domain or to develop new AM tasks. |
| Approach: | They investigate whether multi-task learning can improve performance on AM problems . they found that MTL performs particularly well when little training data is available for the main task . |
| Outcome: | The proposed approach performs better when little training data is available for the main task, a common scenario in AM. |
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning (D19-3)
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Jonas Pfeiffer, Christian M. Meyer, Claudia Schulz, Jan Kiesewetter, Jan Zottmann, Michael Sailer, Elisabeth Bauer, Frank Fischer, Martin R. Fischer, Iryna Gurevych
| Challenge: | Existing systems for technologyenhanced learning address skills on recalling, explaining, and applying knowledge, e.g., in automatically generated language learning exercises and math word problems. |
| Approach: | They propose to leverage a NLP model to support experts in their further data annotation with automatic suggestions and provide automatic feedback for students. |
| Outcome: | The proposed system improves on two user studies on diagnostic reasoning in medicine and teacher education and can be extended to further use cases. |
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems (N19-1)
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Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych
| Challenge: | Recent studies show that visual similarity can play a decisive role in assessing the meaning of characters. |
| Approach: | They investigate the impact of visual adversarial attacks on current NLP systems . they explore three shielding methods that significantly improve the robustness of the models . |
| Outcome: | The proposed methods improve performance but still fall behind non-attack scenarios. |