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Greedy Search with Probabilistic N gram Matching for Neural Machine Translation

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Figure

Figure 1: The overview of our model with greedy search. At each decoding step, the predicted word which has thehighest probability in the probability vector is selected as context and fed into the RNN, and meanwhile this wordand its probability are also used to calculate the probabilistic n-gram count.
Table 1: Results on NIST Chinese-to-English Translation Task. AVG = average BLEU scores for test sets
Figure 2: learning curves of different decoding strate-gies with training objective P-P2.

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