Review for NeurIPS paper: Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding

The motivation for defining "path interstellar" is strong and clearly stated. By comparing the learning ability of triplet-based, path-based, and GCN-based methods, the path interstellar (Definition 1) is proposed as the basic model to learn from KGs. This motivation has also been verified by a case study on synthetic data (experiments in section 4.2). - Domain-specific and well-defined search space. The authors propose a novel recurrent search space specific for the path learning problem. The searched components are either motivated by the models in the literature (combinators, activations) or by the learning problem (connections).