Reviews: Learning to Reason with Third Order Tensor Products

Summary This paper presents a question-answering system based on tensor product representations. Given a latent sentence encoding, different MLPs extract entity and relation representations which are then used to update an tensor product representations of order-3 and trained end-to-end from the downstream success of correctly answering the question. Experiments are limited to bAbI question answering, which is disappointing as this is a synthetic corpus with a simple known underlying triples structure. While the proposed system outperforms baselines like recurrent entity networks (RENs) by a small difference in mean error, RENs have also been applied to more real-world tasks such as the Children's Book Test (CBT). Strengths - I like that the authors do not just report the best performance of their model, but also the mean and variance from five runs.