Name
Abstract | ||
|---|---|---|
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 , and operations. NSE can also access 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. |
Year | Venue | DocType |
|---|---|---|
2016 | Proceedings of the conference. Association for Computational Linguistics. Meeting | Journal |
Volume | Citations | PageRank |
abs/1607.04315 | 21 | 1.02 |
References | Authors | |
22 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsendsuren Munkhdalai | 1 | 169 | 13.49 |
| Hong Yu | 2 | 1982 | 179.13 |