These results provide evidence that literal meanings estimated through pragmatic training are better calibrated for pragmatic usage than meanings estimated through non-pragmatic l 0 training. Furthermore, relative to state-of-the-art in<cite> Monroe et al. (2017)</cite> , Table 2 shows that our pragmatically trained model yields improved accuracy over their best "blended" pragmatic L e model which computed predictions based on the product of two separate non-pragmatically trained models.