99 research outputs found
Textual Membership Queries
Human labeling of data can be very time-consuming and expensive, yet, in many
cases it is critical for the success of the learning process. In order to
minimize human labeling efforts, we propose a novel active learning solution
that does not rely on existing sources of unlabeled data. It uses a small
amount of labeled data as the core set for the synthesis of useful membership
queries (MQs) - unlabeled instances generated by an algorithm for human
labeling. Our solution uses modification operators, functions that modify
instances to some extent. We apply the operators on a small set of instances
(core set), creating a set of new membership queries. Using this framework, we
look at the instance space as a search space and apply search algorithms in
order to generate new examples highly relevant to the learner. We implement
this framework in the textual domain and test it on several text classification
tasks and show improved classifier performance as more MQs are labeled and
incorporated into the training set. To the best of our knowledge, this is the
first work on membership queries in the textual domain.Comment: Accepted to IJCAI 2020. Code is available at
github.com/jonzarecki/textual-mqs . Additional material is available at
tinyurl.com/sup-textualmqs . SOLE copyright holder is IJCAI (International
Joint Conferences on Artificial Intelligence), all rights reserve
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