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• W3044112470 abstract "Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality." @default.
• W3044112470 created "2020-07-29" @default.
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• W3044112470 date "2020-01-01" @default.
• W3044112470 modified "2023-10-10" @default.
• W3044112470 title "A Novel Beam Search to Improve Neural Machine Translation for English-Chinese" @default.
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• W3044112470 doi "https://doi.org/10.32604/cmc.2020.010984" @default.
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