Naively collecting translations by crowdsourcing the task to non-professional translators yields disfluent, low-quality results if no quality control is exercised. We demonstrate a variety of mechanisms that increase the translation quality to near professional levels. Specifically, we solicit redundant translations and edits to them, and automatically select the best output among them. We propose a set of features that model both the translations and the translators, such as country of residence, LM perplexity of the translation, edit rate from the other translations, and (optionally) calibration against professional translators. Using these features to score the collected translations, we are able to discriminate between acceptable and unacceptable translations. We recreate the NIST 2009 Urdu-to-English evaluation set with Mechanical Turk, and quantitatively show that our models are able to select translations within the range of quality that we expect from professional translators. The total cost is more than an order of magnitude lower than professional translation. 1

In this article, an original view on how to improve phrase translation estimates is proposed. This proposal is grounded on two main ideas: first, that appropriate examples of a given phrase should participate more in building its translation distribution; second, that paraphrases can be used to better estimate this distribution. Initial experiments provide evidence of the potential of our approach and its implementation for effectively improving translation performance. 1

MonoTrans2 is a translation system that combines machine translation (MT) with human computation using two crowds of monolingual source (Haitian Creole) and target (English) speakers. We report on its use in the WMT 2011 Haitian Creole to English translation task, showing that MonoTrans2 translated 38 % of the sentences well compared to Google Translate’s 25%. 1

The ongoing rise of human computation as a means of solving computational problems has created an environment where human workers are often regarded as nameless, faceless computational resources. Some people have begun to think of online tasks as a “remote person call”. In this paper, we summarize ethical and practical labor issues surrounding online labor, and offer a set of guidelines for designing and using online labor in ways that support more positive relationships between workers and requestors, so that both can gain the most benefit from the interaction.

In this work, we present a scenario where contextual targeted paraphrasing of sub-sentential phrases is performed automatically to support the task of text revision. Candidate paraphrases are obtained from a preexisting repertoire and validated in the context of the original sentence using information derived from the Web. We report on experiments on French, where the original sentences to be rewritten are taken from a rewriting memory automatically extracted from the edit history of Wikipedia. 1