Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation, which makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e., paraphrases) with only monolingual knowledge of the source language. Formal evaluation demonstrates that this approach can yield substantial improvements in translation quality, and the idea has been integrated into a broader framework for monolingual collaborative translation that produces fully accurate, fully fluent translations for a majority of sentences in a real-world translation task, with no involvement of human bilingual speakers.

In this paper, we report our experience deploying the MonoTrans Widgets system in a public setting. Our work follows a line of crowd-sourced monolingual translation systems, and it is the first attempt to deploy such a system "in the wild". The results are promising, but we also found out that simultaneously drawing from multiple crowds with different expertise and sizes poses unique problems in the design of such crowd-sourcing systems.

In this paper, we present MonoTrans2, a new user interface to support monolingual translation; that is, translation by people who speak only the source or target language, but not both. Compared to previous systems, MonoTrans2 supports multiple edits in parallel, and shorter tasks with less translation context. In an experiment translating children's books, we show that MonoTrans2 is able to substantially close the gap between machine translation and human bilingual translations. The percentage of sentences rated 5 out of 5 for fluency and adequacy by both bilingual evaluators in our study increased from 10% for Google Translate output to 68% for MonoTrans2.