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

Changing the World by Changing the Data