{"id":3152,"date":"2022-09-15T15:19:00","date_gmt":"2022-09-15T13:19:00","guid":{"rendered":"https:\/\/phrase.com\/?p=3152"},"modified":"2023-12-06T13:15:48","modified_gmt":"2023-12-06T12:15:48","slug":"machine-translation-post-editing-best-practices","status":"publish","type":"post","link":"https:\/\/phrase.com\/blog\/posts\/machine-translation-post-editing-best-practices\/","title":{"rendered":"Machine Translation Post-Editing: Perspectives and Best Practices"},"content":{"rendered":"\n
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As artificial intelligence<\/a> increasingly finds its place in daily affairs, machine translation (MT) continues to improve speed, accuracy, and cost-effectiveness. However, it still has some progress to make before reaching parity with human translators.<\/span><\/p>\n

That’s where machine translation post-editing (MTPE) comes into play by combining the best of both worlds\u2014the ability of MT engines to quickly handle large volumes of text with the skill and human touch brought by professional linguists.<\/p>\n

If you’re looking to harness the power of MTPE in your translation workflows, then this guide is your roadmap to success. Dive in for practical tips and best practices to embrace this effective hybrid translation method and unlock productivity gains.<\/span><\/p>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

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What is machine translation post-editing?<\/h2>\n

It’s only in recent years that machine translation has advanced to a point where it can be used for more than just getting a general idea of the meaning of a text. The rise of neural machine translation<\/a> (NMT) has seen a significant improvement in accuracy and fluency for many language pairs, but it still falls short of human-level quality for most use cases. This is where machine translation post-editing comes in.<\/p>\n

Machine translation post-editing is the process of reviewing and improving machine-translated text to make it more accurate and fluent.<\/strong><\/p>\n

In other words, once the machine translation software<\/a> generates text in the target language, professional linguists review the output and translation memory matches to ensure the highest possible quality.<\/p>\n

Perspectives on machine translation post-editing<\/span><\/h2>\n

With continuous improvements in quality, MTPE is now one of the core advantages of machine translation<\/strong> as a viable alternative to human translation. This has had a direct impact on the translation and localization industry.<\/span><\/p>\n

From translation buyers to vendors and linguists, everyone involved has had to adapt to this hybrid translation approach\u2014and despite varying levels of adoption, the general agreement is that MTPE is here to stay<\/strong>.<\/span><\/p>\n

Let’s delve deeper into the various perspectives that influence the adoption of MTPE translation among buyers, vendors, and linguists.<\/span><\/p>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

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The buyer perspective on MTPE<\/span><\/h3>\n

Modern businesses have come to see machine translation<\/a> as a viable productivity tool for entering new markets more quickly while keeping costs low. The advent of neural machine translation has particularly prompted them to leverage their own language data for global growth. Today, the question for most global enterprises is not “if” but “how” they can integrate MT into their localization ecosystem.<\/span><\/p>\n\n\n\n\n\n
Key challenges translation buyers face in machine translation and MTPE<\/td>\n<\/tr>\n<\/thead>\n
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How to integrate MT into an existing translation workflow?<\/p>\n

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What type of content is most suitable for MT?<\/p>\n

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Which machine translation provider to choose?<\/p>\n

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Which language pairs to focus on when using MT?<\/p>\n

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In what ways can MTPE be effectively used and at what price?<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Narrowing the perspective down to MTPE, one of the central considerations for large enterprises is how to allocate a budget for MTPE and use it efficiently in the long run.<\/span><\/p>\n

Although significant for the predictability <\/span>of MTPE-related costs<\/span>, cost-effectiveness is mostly not a goal in itself. On the contrary, saving costs on some machine translation projects with MTPE is considered a means of freeing up a budget to support further language pairs or content types for even greater scalability.<\/span><\/p>\n

All these challenges make the human element of applying MTPE to translation workflows an important factor in the decision-making process.<\/span><\/p>\n

For example, if a company does not have in-house expertise and resorts to outsourcing, it would usually rely on its existing language service provider<\/a> (LSP) or engage a new one as a consultant. The LSP would then deliver a proof of concept on all strategic, linguistic, and technological aspects of integrating machine translation and using MTPE.<\/span><\/p>\n

The vendor perspective on MTPE<\/span><\/h3>\n

The changing demand in the translation and localization market has led many medium to large LSPs to add MT post-editing services to their offerings. This not only makes their portfolio more attractive to buyers and distinguishes them from competitors but also helps them meet their customers’ goals more quickly and consistently provide value.<\/p>\n\n\n\n\n\n
Vendor-side MTPE requires understanding\u2026<\/td>\n<\/tr>\n<\/thead>\n
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What machine translation engines are available on the market<\/p>\n

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How each MT engine performs on different kinds of text and language pairs<\/p>\n

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Which MT engine integrates best with the translation management system used<\/p>\n

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How familiar individual translators are with the MTPE process, etc.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

An experienced post-editor can quickly turn high-quality MT output into a polished translation, but someone who is new to MTPE will need more time\u2014and if the machine translation is sloppy to begin with, even the best post-editor may be unable to salvage it in a reasonable amount of time.<\/span><\/p>\n

That’s why, if LSPs want to provide value to their customers, they need to have training in place for their linguists to continuously improve their post-editing skills.<\/span><\/p>\n

The translator perspective on MTPE<\/span><\/h3>\n

Depending on the clients they work with and the types of projects they specialize in, translators may have little to no experience with MTPE. With machine translation being the new normal, many translators are still trying to adopt a clear attitude towards MTPE.<\/span><\/p>\n

Forward-thinking early adopters have come to trust MT as a productivity tool which they need to be able to adjust to the new demand created on the translation market and complete translation assignments more quickly.<\/span><\/p>\n

As translators gain experience with MTPE, they develop an understanding of how MT performs on different kinds of texts. This helps them estimate the effort that any given MTPE translation project would require from them.<\/span><\/p>\n

They also like to have access to a sample of the text that they can use to judge the quality of the MT output. They use this information to determine whether to accept or reject a project.<\/span><\/p>\n

For example, translators are likely to accept MTPE translation projects involving legal documents, as state-of-the-art MT engines translate them reasonably well. When translators choose to accept a project, they can also use this information to decide what rate to charge.<\/span><\/p>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

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The value of pre-editing: Quality source text saves on post-editing<\/h2>\n

One of the critical initial steps in post-editing machine translation occurs right at the beginning: ensuring you have a tidy input to get a high-quality output.<\/span><\/p>\n

MT works best with clear and concise input. Put another way\u2014t<\/strong>he better the original MT engine output, the less effort needed during post-editing<\/strong>\u2014resulting in quicker turnaround times and reduced costs.<\/span><\/p>\n

As early errors can multiply and lead to issues down the line, the source text should be created or pre-edited carefully for both the MT engine and post-editors to have the best material to work with.<\/span><\/p>\n

That’s why it’s important to ensure that the:<\/span><\/p>\n