Papers by Christian Luhmann
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge (2023.acl-long)
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Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, Christian Luhmann, H. Andrew Schwartz
| Challenge: | Active learning has been proposed to alleviate data acquisition challenges for rare-class tasks when the class label is very infrequent (e.g., 5% of samples). |
| Approach: | They propose to use transformers to train models on closely related tasks and evaluate acquisition strategies, including a proposed probability-of-rare-class approach to dissonance detection. |
| Outcome: | The proposed method improves model accuracy while iterative transfer-learning does not improve cold-start performance. |
Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm (2025.naacl-short)
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Vasudha Varadarajan, Syeda Mahwish, Xiaoran Liu, Julia Buffolino, Christian Luhmann, Ryan L. Boyd, H. Schwartz
| Challenge: | While NLP models often capture cognitive states via language, validity of predicted states is determined by comparing annotations created without access to the cognitive states of the authors. |
| Approach: | They propose a framework for evaluating language-based cognitive style models against human behavior by using an experiment-based framework. |
| Outcome: | The proposed framework shows that language features can predict participants’ decision style with moderate-to-high accuracy (AUC 0.8), demonstrating that cognitive style can be partly captured and revealed by discourse patterns. |