In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa el al.. 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system's output.
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机译:在本文中,我们将提交系统(Geoduck)向第6次研讨会(Wat)(Nakazawa El .. 2019)报告给第6次研讨会的及时披露任务。 我们的系统采用了翻译记忆库和神经机翻译(NMT)模型的组合方法,当测试源句子的相似度得分超过预定义阈值时,我们可以从翻译记忆库或NMT系统中选择最终的转换输出。 我们观察到,与独立NMT系统相比,这种组合方法显着提高了及时披露语料库的平移性能。 我们还对最终产出进行了基于源的直接评估,我们讨论了人类参考和每个系统的输出之间的比较。
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