Papers by Michael Sailer
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. |
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. |