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• W2733056802 abstract "Neural machine translation models rely on the beam search algorithm for decoding. In practice, we found that the quality of hypotheses in the search space is negatively affected owing to the fixed beam size. To mitigate this problem, we store all hypotheses in a single priority queue and use a universal score function for hypothesis selection. The proposed algorithm is more flexible as the discarded hypotheses can be revisited in a later step. We further design a penalty function to punish the hypotheses that tend to produce a final translation that is much longer or shorter than expected. Despite its simplicity, we show that the proposed decoding algorithm is able to select hypotheses with better qualities and improve the translation performance." @default.
• W2733056802 created "2017-07-14" @default.
• W2733056802 creator A5050229964 @default.
• W2733056802 creator A5077805110 @default.
• W2733056802 date "2017-07-06" @default.
• W2733056802 modified "2023-09-27" @default.
• W2733056802 title "Single-Queue Decoding for Neural Machine Translation" @default.
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• W2733056802 hasPublicationYear "2017" @default.
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