  NLP community is currently investing a lot more research and resources into
development of deep learning models than training data. While we have made a
lot of progress, it is now clear that our models learn all kinds of spurious
patterns, social biases, and annotation artifacts. Algorithmic solutions have
so far had limited success. An alternative that is being actively discussed is
more careful design of datasets so as to deliver specific signals. This
position paper maps out the arguments for and against data curation, and argues
that fundamentally the point is moot: curation already is and will be
happening, and it is changing the world. The question is only how much thought
we want to invest into that process.
