This paper evaluates the usefulness of Moses, an open source statistical machine translation (SMT) engine, for professional translators and post editors. It takes a look behind the scenes at the workings of Moses and reports on experiments to investigate how translators can contribute to advances in the use of SMT as a tool. In particular the difference in quality of output was compared as the ...

Commercial off-the-shelf machine translation engines and translation support tools, such as translation memory (TM), have been developed primarily for translating grammatically well-formed, edited text. The real-world, foreign language (FL) document collections that our translators work with consist instead of noisy and complex image files. We are currently conducting experiments that involve b...

Adobe recently began exploring the integration of Machine Translation (MT) technology into its localization workflow. The primary question that we sought to answer was whether post-editing Machine Translation output was faster than translating the text from scratch. The exploration occurred in two stages: a small pilot followed by a larger project localizing Adobe product documentation. The pro...

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 prop...

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 prop...

Vendors of commercial machine translation systems will often claim that their system can increase translator productivity x-fold. In order to verify such claims, we need to answer the following two questions: First, how is translator productivity generally measured? And second, precisely how does one go about comparing human translator (HT) productivity with MT productivity? The answer to the f...

Machine Translation (MT) is said to be the next lingua franca. With the evolution of new technologies and the capacity to produce a humungous number of written digital documents, human translators will not be able to translate documentation fast enough. However, some applications require a level of quality that is still beyond that provided by MT. Thanks to the increased capacity of communicati...

In this paper, I suggest several ways to improve machine translation, based on the best practices of human translators, as described in Nida’s (1964) Toward a Science of Translating. I call this approach multi-pass machine translation (MPMT), as it crucially relies on processing the text more than once. It is similar to the opportunistic bricoleur approach of Gdaniec (1999) in that it sets out ...

With the development of the Internet environments, more and more language services become accessible for common people. However, the gap between human translators and machine translators remains huge especially for the domain of localization processes that requires high translation quality. Although efforts of combining human and machine translators for supporting multilingual communication hav...

Abstract Nowadays, in the globalised context which we find ourselves, language barriers can still be an obstacle to accessing information. On occasions, it is impossible satisfy demand for translation by relying only human translators, therefore, tools such as Machine Translation (MT) are gaining popularity due their potential overcome this problem. Consequently, research field constantly growi...