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Found 4 papers, 1 papers with code
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification
Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language.
Improving Low-Resource Question Answering using Active Learning in Multiple Stages
Furthermore, they often yield very good performance but only in the domain they were trained on.
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs.
History distribution matching method for predicting effectiveness of HIV combination therapies
This paper presents an approach that predicts the effectiveness of HIV combination therapies by simultaneously addressing several problems affecting the available HIV clinical data sets: the different treatment backgrounds of the samples, the uneven representation of the levels of therapy experience, the missing treatment history information, the uneven therapy representation and the unbalanced therapy outcome representation.
