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One of the difficulties of neural machine  (NMT) is the recall and appropriate  of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen  examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect $n$-grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call translation  We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved  pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected  pieces. We show our method improves NMT  results up to 6 BLEU points on three narrow domain  tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the  time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation.

Guiding Neural Machine Translation with Retrieved Translation Pieces