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| =Introduction=
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| ==Background Knowledge==
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| *NTM
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| '''Neural Machine Translation (NMT)''', which is based on deep neural networks and provides an end- to-end solution to machine translation, uses an '''RNN-based encoder-decoder architecture''' to model the entire translation process. Specifically, an NMT system first reads the source sentence using an encoder to build a "thought" vector, a sequence of numbers that represents the sentence meaning; a decoder, then, processes the "meaning" vector to emit a translation. (Figure 1)<sup>[[#References|[1]]]</sup>
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| [[File:VNFigure1.png|thumb|600px|center|Figure 1: Encoder-decoder architecture – example of a general approach for NMT.]]
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| *Beam Search
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| Decoding process:
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| [[File:VNFigure2.png|thumb|600px|center|Figure 2]]
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| Problem: Choosing the word with highest score at each time step t is not necessarily going to give you the sentence with the highest probability(Figure 2). Beam search solves this problem (Figure 3). Beam search has a size m such that at each time step t, it takes the top m proposal and continues decoding with each one of them. In the end, you will get a sentence with the highest probability not in the word level.
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| [[File:VNFigure3.png|thumb|600px|center|Figure 3]]
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| =References=
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| 1. https://github.com/tensorflow/nmt
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