Neural Semantic Encoders
Tsendsuren Munkhdalai, Hong Tao Yu · 2017
We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders.NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations.NSE can also access 1 multiple and shared memories.In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks.For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.