Task: Summarization
Input (English): Real-world Question Answering (QA) tasks consist of thousands of words that often represent many facts and entities. Existing models based on LSTMs require a large number of parameters to support external memory and do not generalize well for long sequence inputs. Memory networks attempt to address these limitations by storing information to an external memory module but must examine all inputs in the memory. Hence, for longer sequence inputs the intermediate memory components proportionally scale in size resulting in poor inference times and high computation costs.

 In this paper, we present Adaptive Memory Networks (AMN) that process input question pairs to dynamically construct a network architecture optimized for lower inference times. During inference, AMN parses input text into entities within different memory slots. However, distinct from previous approaches, AMN is a dynamic network architecture that creates variable numbers of memory banks weighted by question relevance. Thus, the decoder can select a variable number of memory banks to construct an answer using fewer banks, creating a runtime trade-off between accuracy and speed. 

 AMN is enabled by first, a novel bank controller that makes discrete decisions with high accuracy and second, the capabilities of a dynamic framework (such as PyTorch) that allow for dynamic network sizing and efficient variable mini-batching. In our results, we demonstrate that our model learns to construct a varying number of memory banks based on task complexity and achieves faster inference times for standard bAbI tasks, and modified bAbI tasks. We achieve state of the art accuracy over these tasks with an average 48% lower entities are examined during inference.
Output (English): Memory networks with faster inference