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Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants. (arXiv:2112.09062v2 [cs.CL] UPDATED)
May 12, 2022, 1:11 a.m. | Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, Douwe Kiela
cs.CL updates on arXiv.org arxiv.org
In Dynamic Adversarial Data Collection (DADC), human annotators are tasked
with finding examples that models struggle to predict correctly. Models trained
on DADC-collected training data have been shown to be more robust in
adversarial and out-of-domain settings, and are considerably harder for humans
to fool. However, DADC is more time-consuming than traditional data collection
and thus more costly per annotated example. In this work, we examine whether we
can maintain the advantages of DADC, without incurring the additional cost. To …
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