各种音译模式的比较

NEWS@ACL Pub Date : 2018-07-01 DOI:10.18653/v1/W18-2412
Saeed Najafi, B. Hauer, Rashed Rubby Riyadh, Leyuan Yu, Grzegorz Kondrak
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引用次数: 10

摘要

我们在NEWS 2018音译共享任务的背景下报告我们的实验结果。我们重点比较了几种不同的系统,包括三种神经机器翻译模型。判别、生成和神经模型的结合在发展集上获得了最好的结果。我们还提出了改进共享任务的设想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Assorted Models for Transliteration
We report the results of our experiments in the context of the NEWS 2018 Shared Task on Transliteration. We focus on the comparison of several diverse systems, including three neural MT models. A combination of discriminative, generative, and neural models obtains the best results on the development sets. We also put forward ideas for improving the shared task.
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