  We introduce RelNet: a new model for relational reasoning. RelNet is a memory
augmented neural network which models entities as abstract memory slots and is
equipped with an additional relational memory which models relations between
all memory pairs. The model thus builds an abstract knowledge graph on the
entities and relations present in a document which can then be used to answer
questions about the document. It is trained end-to-end: only supervision to the
model is in the form of correct answers to the questions. We test the model on
the 20 bAbI question-answering tasks with 10k examples per task and find that
it solves all the tasks with a mean error of 0.3%, achieving 0% error on 11 of
the 20 tasks.
