  The field of natural language processing (NLP) has grown over the last few
years: conferences have become larger, we have published an incredible amount
of papers, and state-of-the-art research has been implemented in a large
variety of customer-facing products. However, this paper argues that we have
been less successful than we should have been and reflects on where and how the
field fails to tap its full potential. Specifically, we demonstrate that, in
recent years, subpar time allocation has been a major obstacle for NLP
research. We outline multiple concrete problems together with their negative
consequences and, importantly, suggest remedies to improve the status quo. We
hope that this paper will be a starting point for discussions around which
common practices are -- or are not -- beneficial for NLP research.
