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cs.CL2025
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey
Julia Romberg, Christopher Schröder, Julius Gonsior +2
Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an ite…
cs.CL2021
We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processing
Fredrik Olsson, Magnus Sahlgren
In this paper, we identify the state of data as being an important reason for failure in applied Natural Language Processing (NLP) projects. We argue that there is a gap between ac…