Papers by Shalaka Satheesh
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis (2022.lrec-1)
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Michael Gref, Nike Matthiesen, Sreenivasa Hikkal Venugopala, Shalaka Satheesh, Aswinkumar Vijayananth, Duc Bach Ha, Sven Behnke, Joachim Köhler
| Challenge: | Sentiment analysis and emotion recognition can help research in audiovisual interview archives . however, humans perceive sentiments and emotions ambiguously and subjectively . |
| Approach: | They investigate human perceptions of emotions and sentiments in oral history interviews . they show that human perception for different emotions is ambiguous and subjective . authors propose deep learning as a way to categorize and search emotions . |
| Outcome: | The proposed techniques can be used to search and index audiovisual interviews . the authors show that human perceptions differ for different emotions . |
Robustness Evaluation of the German Extractive Question Answering Task (2025.coling-main)
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| Challenge: | Existing evaluation benchmarks for Question Answering systems only include EM and F1 scores, but they overlook critical factors for the deployment of QA systems. |
| Approach: | They propose to define an evaluation method specifically tailored to the German language to evaluate the robustness of German QA models. |
| Outcome: | The proposed method extends existing methods to German language . it shows that all models are vulnerable to character-level perturbations . |
Can Continual Pretraining Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain? (2026.acl-long)
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Niclas Doll, Jasper Schulze Buschhoff, Shalaka Satheesh, Hammam Abdelwahab, Héctor Allende-Cid, Katrin Klug
| Challenge: | specialized models have a large potential for translation and translation, but they lack the integration of domainspecific knowledge and terminology into clinical workflows. |
| Approach: | They construct a German medical corpus to continuously pre-train and merge three well-known LLMs and use it to improve model performance. |
| Outcome: | The proposed model family significantly outperforms the mistral-Small-24B-Instruct model family on German medical benchmarks. |