  We perform trend detection on two datasets of Arxiv papers, derived from its
machine learning (cs.LG) and natural language processing (cs.CL) categories.
Our approach is bottom-up: we first rank papers by their normalized citation
counts, then group top-ranked papers into different categories based on the
tasks that they pursue and the methods they use. We then analyze these
resulting topics. We find that the dominating paradigm in cs.CL revolves around
natural language generation problems and those in cs.LG revolve around
reinforcement learning and adversarial principles. By extrapolation, we predict
that these topics will remain lead problems/approaches in their fields in the
short- and mid-term.
