Papers by Christopher Schröder
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers (2022.findings-acl)
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| Challenge: | a recent study has investigated how transformer-based language models can be combined with active learning. |
| Approach: | They propose to combine transformer-based language models with active learning to reduce labeling costs . transformers are expensive, but they can be fine-tuned using a query strategy . they compare transformers to experiments from previous research to evaluate their performance . |
| Outcome: | The proposed model outperforms the well-known prediction entropy query strategy on five widely used text classification benchmarks. |
Small-Text: Active Learning for Text Classification in Python (2023.eacl-demo)
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| Challenge: | small-text is an easy-to-use active learning library for text classification . it features a variety of pre-implemented state-of-the-art query strategies and stopping criteria . |
| Approach: | They introduce small-text, an easy-to-use active learning library for Python . it offers pool-based active learning for single- and multi-label text classification . they find it matches vanilla transformer fine-tuning in terms of classification accuracy . |
| Outcome: | The proposed library outperforms vanilla transformer fine-tuning in classification accuracy and area under the curve. |
Trigger Warning Assignment as a Multi-Label Document Classification Problem (2023.acl-long)
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| Challenge: | a trigger warning is used to warn people about potentially disturbing content . a webis dataset of 1 million fanfiction works contains up to 36 different warnings per document . |
| Approach: | They introduce a multi-label task to assign a trigger warning to fanfiction . they map 41 million free-form tags assigned by authors into a single taxonomy of trigger warnings . |
| Outcome: | The proposed model achieves micro-F1 scores of about 0.5, which reveals the difficulty of the task. |
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models (2024.emnlp-main)
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| Challenge: | Existing methods to train models without labeled data are lacking in supervised tasks . a lack of labeles is the main obstacle to real-world applications . |
| Approach: | They propose a semi-supervised approach that uses a model to obtain pseudo-labels for unlabeled data. |
| Outcome: | The proposed method outperforms the reproduced methods on four text classification benchmarks. |
Trigger Warnings: Bootstrapping a Violence Detector for Fan Fiction (2023.findings-emnlp)
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| Challenge: | Existing guidelines for proactively alerting readers of potentially disturbing content have been proposed. |
| Approach: | They propose to use a labeled corpus of narrative fiction from a popular fan fiction site to determine whether to assign a trigger warning to an English story. |
| Outcome: | The proposed task achieves F1 scores between 0.8 and 0.9 on three datasets . the authors show that assigning trigger warnings for violence is feasible . |
Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning (2021.acl-long)
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Christopher Schröder, Kim Bürgl, Yves Annanias, Andreas Niekler, Lydia Müller, Daniel Wiegreffe, Christian Bender, Christoph Mengs, Gerik Scheuermann, Gerhard Heyer
| Challenge: | open pit mines left many regions worldwide inhospitable or uninhabitable . aforementioned information has to be acquired to ensure safety and validity of land reuse . |
| Approach: | They propose a workflow for supporting the post-mining management of former open pit mines in the eastern part of Germany . they use active learning to perform multi-label sentence classification for two categories of restrictions and seven categories of topics . |
| Outcome: | The proposed system supports the post-mining management of former lignite open pit mines in the eastern part of Germany. |
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey (2026.eacl-long)
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| Challenge: | a longstanding strategy to reduce annotation costs is active learning . data annotation is expected to remain important and active learning to stay relevant . |
| Approach: | They conduct an online survey to assess the perceived relevance of data annotation and active learning . they propose a strategy to reduce annotation costs using active learning, an iterative process . |
| Outcome: | The proposed strategies reduce setup complexity and uncertainty cost while maintaining model performance. |