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One of the justifications for research on translation processes is that it will

Duolingo, a free online language learning site, has as its mission to help users to learn a language while simultaneously using their learning exercises to translate the web. Language is learned through translation with, according to developers, Duolingo being as effective as any of the leading language learning software. For translating the web, machine translation is not good enough and relying only on professional translators, far too expensive. Duolingo, we are told, offers a third way, with translation as a by-product of its language learning. Translation which will be, if as promised, almost as cheap as if done by machines and almost as good as if by professionals. Launched in June 2012, Duolingo boasts already at the time of writing 300,000 active language learners ready for the task. This article independently assesses the extent to which Duolingo, at its current stage of development, meets those expectations.

Translation memory tools now offer the translator to insert post-edited machine translation segments for which no match is found in the databases. The Google Translator Toolkit does this by default, advising in its Settings window: "Most users should not modify this". Post-editing of no matches appears to work on engines trained with specific bilingual data on a source written under controlled language constraints. Would this, however, work for any type of task as Google's advice implies? We have tested this by carrying out experiments with English-Chinese trainees, using the Toolkit to translate from the source text (the control group) and by post-editing (the experimental group). Results show that post-editing gains in productivity are marginal. With regard to quality, however, post-editing produces significantly better statistical results compared to translating manually. These gains in quality are observed independently of language direction, text difficulty or translator's level of performance. In light of these findings, we discuss whether translators should consider post-editing as a viable alternative to conventional translation.

在机器翻译和协作翻译（众包）的夹击之下，网络专业译员的工作量似乎在减少。为了保持其专业地位，翻译人员必须选择专门从事受到高度监管的领域或成为语言质量控制方面的专家。