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Found 4 papers, 3 papers with code
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models
Despite achieving state-of-the-art results in nearly all Natural Language Processing applications, fine-tuning Transformer-based language models still requires a significant amount of labeled data to work.
ImitAL: Learned Active Learning Strategy on Synthetic Data
Basically, most of the the existing AL strategies are a combination of the two simple heuristics informativeness and representativeness, and the big differences lie in the combination of the often conflicting heuristics.
ImitAL: Learning Active Learning Strategies from Synthetic Data
Active Learning (AL) is a well-known standard method for efficiently obtaining labeled data by first labeling the samples that contain the most information based on a query strategy.
RETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data
Thus, we argue to additionally incorporate the information given by the database schema into the embedding, e. g. which words appear in the same column or are related to each other.
