Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
The reviewers have different views on this paper - although the experimental results are not very strong the paper is well-written and introduces some interesting new ideas to the NeurIPS community. Overall I think the paper is worth presenting at NeurIPS. However, the final camera-ready paper MUST discuss the relation to heirarchical embedding schemes such as [1,2], as discussed by (R3) and also logic tensor networks , a related formalism for embedding logical expressions.  Poincaré Embeddings for Learning Hierarchical Representations. Maximilian Nickel, Douwe Kiela. NIPS, 2017.  Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. Maximilian Nickel, Douwe Kiela. ICML, 2018.  Serafini, Luciano, and Artur d'Avila Garcez. "Logic tensor networks: Deep learning and logical reasoning from data and knowledge." arXiv preprint arXiv:1606.04422 (2016).