Recurrent Neural Networks (RNN) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success remains a challenge. In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data. We demonstrate the power of RMN on language modeling and sentence completion tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM) network on three large German, Italian, and English dataset. Additionally we perform in-depth analysis of various linguistic dimensions that RMN captures. On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state-of-the-art by a large margin.

本文提出了一种新的循环神经网络体系结构Recurrent Memory Network（RMN），不仅能够放大循环神经网络的作用，而且有助于我们理解其内部功能并发现数据中的潜在模式。在语言建模和句子完成任务上展示了RMN的强大性能。在长句完成挑战中，RMN的准确性为69.2％，超过了以前的最新技术水平。

循环记忆网络语言建模