Learning to Control Latent Representations for Few-Shot Learning of Named Entities
Authors: Omar U. Florez, Erik Mueller
Keywords: Memory management, neuroscience, reinforcement learning, learning with small data
TL;DR: We want to learning with small data by introducing a RL trainable controller that learn to write and read in an external memory.
Abstract: Humans excel in continuously learning with small data without forgetting how to solve old problems.
However, neural networks require large datasets to compute  latent representations across different tasks while minimizing a loss function. For example, a natural language understanding (NLU) system will often deal with emerging entities during its deployment as interactions with users in realistic scenarios will generate new and infrequent names, events, and locations. Here, we address this scenario by introducing a RL trainable controller that disentangles the  representation learning  of a  neural encoder from its memory management role. 

Our proposed solution is straightforward and simple: we train a controller to execute an optimal sequence of read and write operations on an external memory with the goal of  leveraging  diverse activations from the past and provide accurate predictions. Our approach is named Learning to Control (LTC) and allows few-shot learning with two degrees of memory plasticity.  We experimentally show that our system obtains accurate results for few-shot learning of entity recognition in the Stanford Task-Oriented Dialogue dataset.