Machine Translation and Professional Translators Community

During the last couple of years, machine translation post-editing has become one of the hottest most discussed topics in the translation industry as evidenced by conferences, forums and webinars. What is the motivation driving this new found interest?

Translators are motivated to use machine translation output since the quality of engines has reached the point where using them leads to proven productivity gains, ranging between 30 and 300%. Thus, machine translation is becoming a commonplace productivity tool similar to translation memories. The emergence of several post-editing standards that are tied to the desired quality levels of the final output allows translators to have additional income opportunities translating content that previously remained monolingual.

Machine translation is still not perfect, and there are recurring challenging issues for post-editors such as lexical coverage, word order, compound formation, word form agreement, omissions and several more. However, a recent positive development, established feedback loops back to the MT engine developers and deployment managers, gives translators more confidence in future engine improvements.
Just as with human translation, post-editing throughput can vary and depends on:

AMTA is actively supporting machine translation as a productivity tool both for language service providers and freelance translators. In all of our past conferences, members of the professional translation community presented their findings on MT adoption as a part of the commercial user track (URL to the past conference proceedings). Synchronizing AMTA and ATA (American Translators Association) conferences for several years helped both organizations drive attendance and draw interest to the topic of machine translation as a productivity tool for translators.

Training professional post-editors 

What is post-editing?

           The “term used for the correction of machine translation output by human linguists/editors” (Veale and Way 1997)

          “Checking, proof-reading and revising translations carried out by any kind of translating automaton”. (Gouadec 2007)

The choice of whether to translate from scratch (“human translation”) or post-edit machine-translated output is driven by the suitability of the source content for machine translation. <>To-date the professional translators community has reported to be achieving the best results with post-editing  the more formal, organized and structured content types with repetitive syntactic patters and predictable use of terminology, which makes them easier for the machine translation engines to handle:

•   Contracts
•   Patents
•   Annual Corporate Reports
•   Light Marketing (as opposed to “transcreation”)
•   Software Documentation
•   Software User Interface
•   SEO (Search Engine Optimization) keywords
•   e-Learning Content
•   User Guides and Product Manuals
•   Internal Corporate Communications
•   Wikis
•   Knowledge Bases
•   Proposals / Draft Applications

The decision on which post-editing quality level to select is mainly determined by the visibility, perishability and the target audience for the content, or “utility”, which is the term lately adopted by the industry. The content utility also dictates the number of errors and the error type tolerance for the given content type. As outlined in the TAUS post-editing guidelines, it is essential that the post-editors are given very explicit and clear set of instructions that describe the desired quality levels.

At the moment two levels of post-editing are recognized as industry standard: “good enough”, often referred to as “light post-editing”, and “publishable”, where the final output quality is expected to be on par with the translation performed from scratch.

The table below is an example of two levels of post editing:

The best results are achieved when post editors and machine translation engine developers are in a continuos constructive dialog around the challenges translators are facing when post editing the MT output. This both allows the post-editors to build the sense of ownership of the engines, and helps the developers to tailor their engine roadmap to the actual needs of translators.
While the “adequacy” – related feedback helps with selecting the appropriate engine training data, the “fluency” and “readability” feedback helps with fine-tuning the core engine functionality, including the language-related and the engineering issues, such as handling of metadata and locale-specific conventions.

Recognizing the need for developing a skilled and qualified post-editors workforce, several major Language Service Providers and now publish their “how-to” introductory courses on the principles and best practices for post-editing machine translation output:
Welocalize:  http://www.welocalize.com/wemt/machine-translation-overview/

SDL: http://www.sdl.com/campaign/lt/eBooks/language-technology-ebooks-tab2.html#tabs

Hunnect: http://www.hunnectacademy.com/en/content/content/7

Industry Initiatives on Post-editing Machine Translation Output

Below are URLs to some of the industry initiatives relevant to the use of machine translation as a post-editing productivity tool. The list in maintained in a “work in progress” mode and is being updated regularly.

TAUS Post-Editing Guidelines (created in partnership with CNGL): general post-editing guidelines for “good enough” and “human translation level” post-editing. TAUS has also recently published guidelines on pricing post-editing work and measuring post-editors’ productivity.

TAUS Dynamic Quality Framework: a set of tools and methodologies for evaluating post-editors’ productivity, selecting a machine translation engine most suitable for a specific project and reviewing machine translation and post-editing errors in a structured environment.

QTLaunchpad: European Commission-funded collaborative research initiative dedicated to overcoming quality barriers in machine and human translation and language technologies.

2013 MT Summit Workshop on Post-Editing Technologies and Practice: a recent workshop on post-editing organized by Dr. Sharon O’Brien, Michel Simard and Lucia Specia (follow the URLs to their professional web pages to see more publications).

There are several LinkedIn groups dedicated to post-editing of machine translation output and other translation automation tools:

Automated Language Translation (MT/Machine Translation): the group focusing on discussions around the trends and developments in machine translation, with the members both from the development and user side.

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