Papers by Andreas Fischer
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. |
Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German (2020.lrec-1)
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| Challenge: | Despite the small pool of speakers, there are still few natural language processing corpora, studies or tools for Swiss German. |
| Approach: | They propose to use a web scraper to generate the largest Swiss German text corpus . they show that the tool can be applied to other low-resource languages as well . |
| Outcome: | The proposed tool significantly improves language modeling in Swiss German, the authors show . |
EdTec-QBuilder: A Semantic Retrieval Tool for Assembling Vocational Training Exams in German Language (2024.naacl-demo)
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| Challenge: | Existing methods to gather test items from validated item databases are under-researched, but there is little research on assembling exam items from a database of valid items. |
| Approach: | They propose to use semantic search to assist vocational educators in assembling exam forms by using eight retrieval strategies and 25 popular sentence similarity models. |
| Outcome: | The proposed tool is based on eight retrieval strategies and 25 popular pre-trained sentence similarity models. |
Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services (2025.naacl-industry)
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| Challenge: | Despite the practical importance of exam assembly, few methods exist to support educators during manual item retrieval for exam assembly tasks. |
| Approach: | They propose a mixed-integer programming re-ranking approach to improve relevance while mitigating bias on an industry-grade exam assembly platform. |
| Outcome: | The proposed approach improves relevance and reduces bias by 17% when compared to other methods on a real-world exam assembly platform. |