Papers by Elisabeth Fischer

4 papers
Assessing the State of the Art in Scene Segmentation (2025.naacl-long)

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Challenge: Recent advances in scene segmentation have made it difficult to detect scenes in literary texts.
Approach: They propose to modify existing models to improve detection of scenes in literary texts . they propose to use a training sample generation scheme to alleviate this problem .
Outcome: The proposed model is more robust to different types of texts, while its overall performance is slightly worse than that of BERT-based models.
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains (P19-1)

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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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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.
AnnoPlot: Interactive Visualizations of Text Annotations (2024.eacl-demo)

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Challenge: Annotation projects face challenges in data quality and validity, authors argue .
Approach: They propose an open-source web application that analyzes, manages, and visualizes annotated text data.
Outcome: The proposed application is open-source and promotes transparency and user control . it offers comprehensive views of span annotations and category systems without training or classification model .

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