How Neural Machine Translation is Transforming Content Marketing in 2023

Scale content creation and save time.

2023 looks to be the year when machine translation becomes an integral fixture of the content marketing landscape. This prediction is supported by top-line data points in Technavio’s in-depth survey of the content marketing industry released in August 2022.

The report anticipates substantial revenue growth at a galloping 15.8% through 2026, accounting for a massive $487 billion income differential over that 5-year stretch.

Most remarkably, the report projects that a whopping 38% of this growth will come from the APAC market. For the vast majority of content marketing, that massive chunk of change has, until now, been mostly off the table. Which marketers, after all, have the luxury of focusing on reaching a mindshare of the sprawling multilingual markets of Asia and the Pacific, spanning 48 countries? Think China, India, Indonesia, Japan, Thailand, and Vietnam. That’s a total of 4.3 billion people—about half of humanity—of which only a tiny fraction speak English. 

Sorry, Australia and New Zealand. Sheep don’t count.

For content marketers who don’t want to ignore those massive audiences and leave that revenue behind, the question becomes: how can we economically translate our marketable content in a scalable way to these almost exclusively foreign-speaking markets? Until now, content marketers going after these massive but hard-to-access targets have had to staff up internally or hire agencies, country by country, for translation, website localization, and language-specific marketing. That’s expensive and time-consuming in addition to being difficult (if not impossible) to manage at scale.

However, in 2023, machine translation is fast becoming a viable option for massive and automated content translation. What changed? The answer is NMT: neural machine translation. After decades of R&D, NMT is finally reaching a point of market-readiness that is revealing to be a game-changer in several ways. Here are some take-aways about leveraging this fast-maturing technology for the practical tasks of content marketing.

 How does NMT supersede previous iterations of machine translation?

How does NMT supersede previous iterations of Machine Translation

Neural machine translation (NMT) is based on a neural network that uses artificial intelligence to mimic the messaging of the human brain.These neural networks are made up of neurons and synapses that enable high-end computers to catalog and map any given language and accurately convert it to another. 

Each network applies a variety of learning processes, which resemble to some extent the way human beings learn and think. NMT systems are trained using vast quantities of data, aggregating millions of vetted translations completed by human translators, then interpret from that multiplicity of samples the rules of how language is constructed and expressed. 

The GIGO rule of computing—Garbage In, Garbage Out—applies here. One reason why early NMT translations were criticized as dull and lifeless was that the samples used for machine training were often derived from UN and EU parliament documents. 

The samples being used for training have vastly expanded in recent years. Apple began using samples derived from the language iPhone users texted. Meta has been doing the same for social media posts on Facebook. 

Unlike SMT, NMT does not work word-for-word but sentence-by-sentence, or even paragraph by paragraph, evaluating all possible translations of a text and then choosing the optimal way to recreate the intended and contextual meaning. The result is a translation more accurate and natural-sounding than was possible before.

Can free NMT be leveraged for content marketing?

For SMEs, content creators, and marketers, it boils down to whether they will try to get by using free or cheap translation software solutions. The alternative is to hire agencies that specialize in NMT and know how to leverage it in translation projects.

There’s a trio of capable performers that use NMT: Google Translate (supporting 133 languages), Microsoft Translator, and the smaller but equally capable DeepL. Content marketers should check out each of these, just to be familiar with what’s out there. The basic features for all three are free to use, though API applications of the programs for automated translations have a payment wall. 

There still remains a quality gap between the top NMTs and the best human translators. In 2020, the estimated accuracy of machine translations, according to linguistic experts, ranged from 60%-90%, though it’s estimated that NMT improves 3-7% annually. So, by 2023, we should expect NMT accuracy to be somewhere in the range of 72 to 98%.

Where NMTs excel even now, however, can be of keen interest to content marketers seeking to expand markets at a low cost. These use-cases involve documents or software with a limited set of predictable words and jargon. 

  • Technical documentation: user manuals, operating guides, instructions
  • Online commerce content, especially catalogs and product descriptions
  • Business software localization for computers and phones
  • Marketing and financial reports
  • Standard business websites

What this list lacks, of course, is anything creative, imaginative, or persuasive. NMTs do not excel with content intended to play on human emotions. They are better at “cold” and “dry” translations. Don’t entrust them with your branding or marketing campaigns. 

What value can NMT-capable translation agencies bring to content marketing?

What value can NMT-capable translation agencies bring to content marketing

Even for the coldest and driest of documents, you still can’t get by on NMT alone. Always be sure to have a native-speaking translator review each document in each language before it goes out the door. They will be able to provide quality assurance and constructive suggestions for anything translated by an algorithm or another human.

If you are working with an external language service provider (LSP), who are experts in the translation, interpretation, and localization industry, you may want to assign someone internally or take it upon yourself to learn the professional translation process. That way, your team can eventually take over these post-editing tasks.

Learn to do it yourself. Leverage a hired LSP’s consulting smarts and accumulated know-how to get started. This will allow you to gradually acquire independent knowledge on how translation and localization is done. Apply software at the API level for content you need to translate in volume.
Over the course of months and years, you can bring some of the tasks initially performed by an LSP in-house to save your organization time and money. But keep the translation pros in the loop, for QA and consulting as well as help translating documents and software that require a creative flair and emotional impact.

FAQ

What is neural machine translation (NMT) in marketing?

Neural machine translation (NMT) in marketing refers to the use of artificial intelligence (AI) and neural network technology to automatically translate marketing content, such as advertisements, websites, and promotional materials, from one language to another with high accuracy and fluency.

How does neural machine translation differ from traditional translation methods?

Neural machine translation differs from traditional translation methods by utilizing deep learning algorithms to analyze and understand the context of entire sentences or phrases, resulting in more accurate translations that capture nuances and idiomatic expressions.

What are the benefits of using neural machine translation in marketing?

The benefits of using neural machine translation in marketing include faster translation turnaround times, improved translation quality, cost savings compared to manual translation services, and the ability to reach global audiences with localized content.

How can neural machine translation enhance multilingual marketing campaigns?

Neural machine translation enhances multilingual marketing campaigns by providing consistent and high-quality translations across multiple languages, enabling marketers to maintain brand voice and messaging consistency while catering to diverse linguistic audiences.

What types of marketing content can benefit from neural machine translation?

Various types of marketing content can benefit from neural machine translation, including website content, product descriptions, social media posts, email newsletters, advertising copy, and marketing collateral such as brochures and flyers.

Is neural machine translation suitable for all languages?

While neural machine translation has shown significant advancements in translating major languages, its effectiveness may vary depending on language complexity, availability of training data, and linguistic nuances. However, it can still provide valuable assistance in translating a wide range of languages.

How can marketers ensure the accuracy of neural machine translations?

Marketers can ensure the accuracy of neural machine translations by validating translations with native speakers or professional linguists, reviewing translated content for context and cultural appropriateness, and fine-tuning translation models based on feedback and performance metrics.

Can neural machine translation be integrated with marketing automation platforms?

Yes, neural machine translation can be integrated with marketing automation platforms to streamline the translation process, automate content localization workflows, and efficiently manage multilingual marketing campaigns across various channels.

What are some considerations when implementing neural machine translation in marketing?

When implementing neural machine translation in marketing, it’s important to consider factors such as data privacy and security, regulatory compliance with language and advertising standards, potential cultural sensitivities, and ongoing optimization of translation models for improved performance.

How can neural machine translation contribute to global brand expansion and customer engagement?

Neural machine translation contributes to global brand expansion and customer engagement by breaking down language barriers, allowing brands to communicate with international audiences in their native languages, fostering stronger connections, and increasing market reach and penetration.

How does neural machine translation improve translation accuracy over previous methods?

Neural machine translation improves translation accuracy over previous methods by employing deep learning algorithms to understand context, syntax, and semantics, resulting in more natural and fluent translations that better preserve the original meaning.

Can neural machine translation adapt to industry-specific terminology and jargon?

Yes, neural machine translation can adapt to industry-specific terminology and jargon by training translation models on domain-specific data, such as specialized dictionaries, glossaries, or corpora, to ensure accurate and contextually relevant translations for marketing content.

What role does post-editing play in refining neural machine translations for marketing?

Post-editing involves manually reviewing and refining neural machine translations to correct errors, improve fluency, and ensure consistency with brand messaging and tone. It helps enhance translation quality and fine-tune the output for specific marketing objectives.

How can marketers leverage neural machine translation for real-time content localization?

Marketers can leverage neural machine translation for real-time content localization by integrating translation APIs or plugins into content management systems (CMS) or marketing platforms, enabling automatic translation of website updates, blog posts, or social media content on-the-fly.

Are there any limitations or challenges associated with neural machine translation in marketing?

Some limitations and challenges of neural machine translation in marketing include language ambiguity, cultural nuances, potential errors in context-sensitive translations, and the need for ongoing model training and optimization to maintain translation quality.

How does neural machine translation impact multilingual search engine optimization (SEO) strategies?

Neural machine translation can impact multilingual SEO strategies by ensuring translated content is optimized for relevant keywords and search intent in target languages, helping improve search visibility and organic traffic for international audiences.

Can neural machine translation be combined with human translation for marketing content?

Yes, neural machine translation can be combined with human translation for marketing content through a process known as “hybrid translation.” This approach leverages AI for initial translation and human translators for post-editing and refinement, ensuring high-quality, human-readable translations.

What considerations should be made for brand voice and tone when using neural machine translation?

When using neural machine translation, marketers should consider adapting brand voice and tone guidelines for each target language to maintain consistency and authenticity across translations, ensuring they resonate with local audiences while staying true to the brand identity.

How can neural machine translation help marketers expand into new international markets?

Neural machine translation can help marketers expand into new international markets by reducing the time and cost associated with translating marketing content, enabling rapid deployment of localized campaigns, and facilitating communication with global audiences in their native languages.

What future advancements can we expect in neural machine translation for marketing?

Future advancements in neural machine translation for marketing may include improved language coverage, enhanced customization options for domain-specific translation models, integration with voice and image recognition technologies, and further automation of content localization workflows to streamline global marketing efforts.

Author bio

New York-born Richard Koret is a full-time writer and editor, with a degree from Princeton and three decades of experience in content creation, marketing communications and online publishing. He is a serial entrepreneur, and served as COO and CTO of Africana.com, sold to AOL Time Warner for 8 figures. He is a world-wandering nomad currently residing in Thailand.

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