In this paper, we address the problem of verb selection for data-to-text NLG through a principled data-driven approach.
we show how to employ Bayesian reasoning to train a probabilistic model for verb selection based on large-scale realworld news corpora, and demonstrate its advantages over existing verb selection methods.