Barbara Plank
Papers
1
Total Citations
2
H-Index
1
About
Barbara Plank is a leading researcher in natural language processing (NLP) and computational linguistics, with a primary focus on robust and adaptable language technologies. Her work centers on domain adaptation, transfer learning, and cross-lingual NLP, addressing how models can generalize across diverse languages, genres, and tasks. She has made foundational contributions to understanding and mitigating model brittleness, particularly through her research on part-of-speech tagging, dependency parsing, and semantic role labeling under domain shift. Plank is also known for pioneering work in data selection and annotation efficiency, including active learning strategies that reduce the need for labeled data. Her highly cited papers, such as those on domain adaptation for NLP and cross-lingual model transfer, have garnered hundreds of citations each, reflecting their lasting impact on the field. She has been recognized with prestigious awards, including an ERC Starting Grant for her project on "Robust and Efficient Learning for NLP" (REAL-NLP). Currently a professor at LMU Munich, Plank continues to shape the future of NLP by championing reproducibility, open science, and linguistically diverse evaluation.
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Top Papers
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