Post-editing Effort of a Novel With Statistical and Neural Machine Translation
Post-editing Effort of a Novel With Statistical and Neural Machine Translation
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统计和神经机器翻译小说的译后编辑工作
DOI：
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发表时间：
2018
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通讯作者：
Andy Way
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作者：
Antonio Toral;M. Wieling;Andy Way
我们进行了第一个实验，在文学中，小说被自动翻译，然后由专业的文学翻译人员进行后期编辑。我们的案例研究是Warbreaker，这是一本流行的幻想小说，最初是用英语写的，我们把它翻译成加泰罗尼亚语。我们使用两种数据驱动的机器翻译方法翻译了小说的一章（超过3，700个单词，330个句子）：基于短语的统计MT（PBMT）和神经MT（NMT）。这两个系统都是为小说量身定制的;它们接受了超过1亿字的小说训练。在编辑后实验中，六位具有文学翻译经验的专业翻译家在三种交替条件下翻译本章的子集：从头开始（小说翻译行业的规范），编辑后PBMT和编辑后NMT。我们记录所有的停顿，翻译每个句子所用的时间，以及停顿的次数和持续时间。基于这些测量，并使用混合效应模型，我们研究了编辑后的努力在其三个常用的研究维度：时间，技术和认知。我们观察到两种MT方法都提高了翻译效率：PBMT提高了18%，NMT提高了36%。后编辑也导致了重复次数的减少：PBMT减少了9%，NMT减少了23%。最后，关于认知努力，编辑后的结果更少（PBMT和NMT分别减少了29%和42%），但停顿时间更长（14%和25%）。
We conduct the first experiment in the literature in which a novel is translated automatically and then post-edited by professional literary translators. Our case study is Warbreaker, a popular fantasy novel originally written in English, which we translate into Catalan. We translated one chapter of the novel (over 3,700 words, 330 sentences) with two data-driven approaches to Machine Translation (MT): phrase-based statistical MT (PBMT) and neural MT (NMT). Both systems are tailored to novels; they are trained on over 100 million words of fiction. In the post-editing experiment, six professional translators with previous experience in literary translation translate subsets of this chapter under three alternating conditions: from scratch (the norm in the novel translation industry), post-editing PBMT, and post-editing NMT. We record all the keystrokes, the time taken to translate each sentence, as well as the number of pauses and their duration. Based on these measurements, and using mixed-effects models, we study post-editing effort across its three commonly studied dimensions: temporal, technical and cognitive. We observe that both MT approaches result in increases in translation productivity: PBMT by 18%, and NMT by 36%. Post-editing also leads to reductions in the number of keystrokes: by 9% with PBMT, and by 23% with NMT. Finally, regarding cognitive effort, post-editing results in fewer (29 and 42% less with PBMT and NMT, respectively) but longer pauses (14 and 25%).