MEMO: A Deep Network for Flexible Combination of Episodic Memories
Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning.Here we employed a classic associative inference task from the human neuroscience literature in order to more carefully probe the reasoning capacity of existing memory-augmented architectures.This task is thought to capture the essence of reasoning -- the appreciation of distant relationships among elements distributed across multiple facts or memories.Surprisingly, we found that current architectures struggle to reason over long distance associations.Similar results were obtained on a more complex task involving finding the shortest path between nodes in a path.We therefore developed a novel architecture, MEMO, endowed with the capacity to reason over longer distances.This was accomplished with the addition of two novel components.First, it introduces a separation between memories/facts stored in external memory and the items that comprise these facts in external memory.Second, it makes use of an adaptive retrieval mechanism, allowing a variable number of ‘memory hops’ before the answer is produced.MEMO is capable of solving our novel reasoning tasks, as well as all 20 tasks in bAbI.
A memory architecture that support inferential reasoning.This paper proposes changes to the End2End Memory Network architecture, introduces a new Paired Associative Inference task that most existing models struggle to solve, and shows that their proposed architecture solves the task better.A new task (paired associate inference) drawn from cognitive psychology, and proposal for a new memory architecture with features that allow for better performance on the paired associate task.