Differential Translation for Japanese Partially Amended Statutory Sentences
Differential Translation for Japanese Partially Amended Statutory Sentences
复制标题
日语部分修改的法定句子的差异翻译
DOI：
10.1007/978-3-030-79942-7_11
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Toyama Katsuhiko
中科院分区：
文献类型：
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作者：
Yamakoshi Takahiro;Komamizu Takahiro;Ogawa Yasuhiro;Toyama Katsuhiko
我们提出了一个差异翻译方法，目标是部分修改的修正案的法定句子。在法规修订后，我们需要及时更新其翻译，以供国际读者阅读。我们必须注重翻译的重点。换句话说，我们应该只修改译文中被修改的词语，而保留其他词语，以免引起对修改内容的误解。为了生成焦点，流畅和适当的翻译，我们的方法结合了神经机器翻译（NMT）和模板感知统计机器翻译（SMT）。特别是，我们的方法生成的最好的翻译NMT模型与蒙特卡洛辍学，并选择最好的一个比较，他们与SMT翻译。这弥补了每种方法的弱点：NMT翻译通常很流畅，但往往缺乏焦点和充分性，而模板感知的SMT翻译相当集中和充分，但不流畅。在我们的实验中，我们证明了我们的方法优于仅NMT和仅SMT方法。
We propose adifferentialtranslation method that targets statutory sentences partially modified by amendments. After a statute is amended, we need to promptly update its translations for international readers. We must focus on thefocalityof translation. In other words, we should modify only the amended expressions in the translation and retain the others to avoid causing misunderstanding of the amendment’s contents. To generate focal, fluent, and adequate translations, our method incorporates neural machine translation (NMT) and template-aware statistical machine translation (SMT). In particular, our method generatesn-best translations by an NMT model with Monte Carlo dropout and chooses the best one by comparing them with the SMT translation. This complements the weaknesses of each method: NMT translations are usually fluent but they often lack focality and adequacy, while template-aware SMT translations are rather focal and adequate but not fluent. In our experiments, we showed that our method outperformed both the NMT-only and SMT-only methods.