Transcription of Learning Phrase Representations using RNN ... - EMNLP 2014
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Learning Phrase Representations using RNN ... - EMNLP 2014
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ( EMNLP ), pages 1724 1734,October 25-29, 2014, Doha, 2014 Association for Computational LinguisticsLearning Phrase Representations using RNN Encoder Decoderfor Statistical Machine TranslationKyunghyun ChoBart van Merri enboer Caglar GulcehreUniversit e de Montr BahdanauJacobs University, Bougares Holger SchwenkUniversit e du Maine, BengioUniversit e de Montr eal, CIFAR Senior this paper, we propose a novel neu-ral network model called RNN Encoder Decoder that consists of two recurrentneural networks (RNN). One RNN en-codes a sequence of symbols into a fixed-length vector representation, and the otherdecodes the representation into another se-quence of symbols. The encoder and de-coder of the proposed model are jointlytrained to maximize the conditional prob-ability of a target sequence given a sourcesequence.
where f is a non-linear activation func-tion. f may be as simple as an element-wise logistic sigmoid function and as com-plex as a long short-term memory (LSTM) unit (Hochreiter and Schmidhuber, 1997). An RNN can learn a probability distribution over a sequence by being trained to predict the next symbol in a sequence. In that case, the output
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