Recent work on deep learning syntactic parsing models has achieved notably good results, e.g., Dyer et al. (2016) with 92.4 F 1 on Penn Treebank constituency parsing and<cite> Vinyals et al. (2015)</cite> with 92.8 F 1 . In this paper we borrow from the approaches of both of these works and present a neural-net parse reranker that achieves very good results, 93.8 F 1 , with a comparatively simple architecture.