Xinyue Lin1, Jin Liu1, *, Jianming Zhang2, Se-Jung Lim3
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 387-404, 2020, DOI:10.32604/cmc.2020.010984
- 23 July 2020
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
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