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• W2963973721 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 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." @default.
• W2963973721 created "2019-07-30" @default.
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• W2963973721 date "2017-01-01" @default.
• W2963973721 modified "2023-09-26" @default.
• W2963973721 title "Neural Semantic Encoders" @default.
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• W2963973721 doi "https://doi.org/10.18653/v1/e17-1038" @default.
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