{"id":1349,"date":"2017-02-08T00:00:00","date_gmt":"2017-02-08T00:00:00","guid":{"rendered":"https:\/\/www.motionpoint.com\/?post_type=blog&p=1349"},"modified":"2017-02-08T00:00:00","modified_gmt":"2017-02-08T00:00:00","slug":"can-neural-machine-translation-compete-with-human-translation-part-one","status":"publish","type":"blog","link":"https:\/\/www.motionpoint.com\/blog\/can-neural-machine-translation-compete-with-human-translation-part-one\/","title":{"rendered":"Can Neural Machine Translation Compete with Human Translation? (Part 1)"},"template":"","blog_categories":[107,15],"resource_categories":[37,30,35,23,32,22,31,25,17,18],"targeted_localized_audience":[],"document_categories":[],"class_list":["post-1349","blog","type-blog","status-publish","hentry","blog_categories-translation-technology","blog_categories-website-translation","resource_categories-content-type-blog-post","resource_categories-business-goal","resource_categories-content-type","resource_categories-role-marketer","resource_categories-business-goal-optimize-performance","resource_categories-role","resource_categories-business-goal-streamline-operations","resource_categories-role-technologists-developers","resource_categories-topic","resource_categories-topic-website-translation"],"acf":{"blog_short_description":"Recent breakthroughs indicate an intriguing future for machine translation. But is it ready for prime time on global websites?","news_inline_styles":"","blog_video_id":"","article_body":"

Editor\u2019s Note:<\/b> This is Part 1 in a 2-part series. Read Part 2 here.<\/a><\/i><\/p>\n

As the need grows\u2014and accelerates\u2014for companies to serve an increasingly diverse and global customer base online, so too does the need to serve them in their preferred languages.<\/p>\n

We\u2019ve written extensively<\/a> about how launching translated websites in international markets can woo new customers and generate new revenue. And we\u2019ve shared best practices such as \u201cbrand-perfect translations<\/a>\u201d and localization<\/a> that showcase ways businesses can make authentic connections with global consumers.<\/p>\n

MotionPoint has long advocated using human translators to craft this resonant online content. (And we still do; more on this near the end of this post.) But recent technological breakthroughs in machine translation have generated headlines in mainstream media. These innovations have also sparked conversations within growing companies. Is machine translation viable to use in our global online expansion plans?<\/i>, they wonder.<\/p>\n

Perhaps you\u2019ve seen some of these headlines. Within the past six months, a special technology called Neural Machine Translation (NMT)<\/b> has generated gobs of buzz within the translation industry and beyond. <\/p>\n

\n

Is machine translation viable to use in our global online expansion plans?<\/p>\n<\/blockquote>\n

NMT is different from conventional phrase-based statistical machine translation, which has historically been used in services such as Google Translate. As a December New York Times<\/i> story<\/a> put it, NMT uses \u201cartificial \u2018neural networks\u2019 that acquaint themselves with the world via trial and error, as toddlers do,\u201d in an effort to \u201cdevelop something like human flexibility.\u201d The more limber and adaptable this virtual brain can be, experts say, the better its translations will be.<\/p>\n

The recent flurry of coverage about NMT hails from a 20-page paper<\/a> Google published last September. In it, the company revealed breakthroughs that wildly improved the accuracy and readability of NMT translations. The resulting translations left some folks, including one high-profile Japanese professor\u2014an expert on human-computer interactions\u2014\u201castonished,\u201d the recent New York Times<\/i> story said. <\/p>\n

\u201cEverybody wondered,\u201d the story\u2019s author wrote, \u201cHow had Google Translate become so uncannily artful?\u201d<\/p>\n

Recent NMT Innovations<\/h2>\n

So why were folks agog for this Google announcement? For starters, NMT hasn\u2019t been an especially efficient way to translate content, which is why it\u2019s rarely deployed in production environments. Beyond being computationally expensive, it also often falters when it encounters unusual or rare words. And it\u2019s infamous for translating content only some of the time, resulting in weird, incomplete mixed-language translations.<\/p>\n

But in September, Google said its breakthroughs had improved translation quality by 60% to 87%, depending on the language pair, when compared to Google Translate\u2019s performance. Google\u2019s NMT approach appears to play nicely between numerous language pairs. This means it can translate content from<\/i> numerous languages to<\/i> numerous languages with little fuss or adjustment.<\/p>\n

When compared with conventional Google Translate translations, the NMT results also delivered far superior BLEU scores<\/a>, a measurement of machine translation quality. Google researchers claimed NMT\u2019s translation quality was peerless compared to other published results, and at times, \u201chuman and Google Neural Machine Translations are nearly indistinguishable.\u201d<\/p>\n

\n

NMT is infamous for translating content only some of the time, resulting in weird, incomplete mixed-language translations.<\/span><\/p>\n<\/blockquote>\n

Since then, Google has integrated its NMT approach<\/a> into its Google Translate product, into a total of eight language pairs. The languages\u2014which include major European, Latin American and Asian languages\u2014represent the native languages of about one-third of the world\u2019s population, Google says.<\/p>\n

Such moves have long been afoot in the machine translation space. Last January<\/a>, Microsoft announced artificial intelligence-powered changes to its own machine translation API. By leveraging its AI expertise, the company claimed it could now provide quick and easily-customizable translation systems without a customer needing to provide it \u201clarge amounts\u201d of previously-translated content.<\/p>\n

Baidu, China\u2019s primary search engine, is investing heavily here, too. Its machine-translation product Baidu Translate debuted in 2011. The service is based on deep learning technology, a Baidu representative told Slator, a news site covering the Language Service Provider industry, last March<\/a>.<\/p>\n

\u201cAlthough there are still gaps between machine translation and human translation, the quality of machine translation, in some specific domains, is already nearly as good as that of human beings,\u201d asserted a Baidu representative. <\/p>\n

Amazon is also getting in on the action by beefing up its Pittsburgh-based machine translation development team. Interestingly, Amazon isn\u2019t doubling down on NMT like Google. While there\u2019s been \u201cexciting progress\u201d in that field in recent years, it remains in the \u201cearly stages of maturity,\u201d Alon Lavie, a Machine Translation Group Manager for Amazon Pittsburgh, told Slator in January<\/a>.<\/p>\n

Lavie is far from the only professional who\u2019s managing their expectations for NMT. The technology is \u201cstill a long way from \u2018nearly indistinguishable from human translation,\u2019\u201d consultant Kirti Vashee said<\/a> of Google\u2019s September 2015 announcement. <\/p>\n

The Mission-Critical \u2018Human Touch\u2019<\/h2>\n<\/p>\n

Despite the recent extensive NMT coverage, the profession of human translation isn\u2019t going anywhere. If anything, it\u2019s thriving and growing. Based on a 2015 study<\/a> that compiled data from more than 90 government resources, translation and interpretation services are the fastest-growing industry in the U.S. by job growth. The industry will add 36% more jobs\u2014about 12,400\u2014between 2014 and 2019, the study found. Data from a December 2015 U.S. Department of Labor report<\/a> complemented those findings.<\/p>\n

It\u2019s clear human translation isn\u2019t going anywhere, and indeed it shouldn\u2019t. NMT and all other forms of machine translation still can\u2019t hold a candle to the skills and quality that humans bring to translation. MotionPoint has long advocated for using human translators. In fact, we employ their skills extensively in our website translation work with customers.<\/p>\n

When it comes to determining the nuanced creative intent of content, and conveying that intent in another language\u2014using words that deliver far more than information, but also emotion and impact\u2014the human brain reigns supreme. Preserving this intent and resonance is especially important for companies that are concerned with brand voice.<\/p>\n

\n

NMT still can\u2019t hold a candle to the skills and quality that humans bring to translation.<\/p>\n<\/blockquote>\n

We spoke to a few MotionPoint employees about the topic, including Eric Frank, Global Account Director for MotionPoint\u2019s sales team. <\/p>\n

\u201cHuman translation really is the only way to capture the true voice of a brand,\u201d Eric explains. \u201cThere are countless nuances to copywriting\u2014from levels of formality to wordplay to dialect\u2014that translation software still can\u2019t deduce or replicate. It often goes for the \u2018lowest common denominator\u2019 word choice.\u201d<\/p>\n

Out of the box, machine translation rarely takes into account word choices for industries or audiences. Take the automobile industry, Eric says. Some car manufacturers may use the word \u201ccars\u201d to describe their products, while others call them \u201cautomobiles\u201d or \u201c4-wheel passenger vehicles.\u201d Those are deliberate creative choices made by the brand. <\/p>\n

\u201cBut machine translation doesn\u2019t understand that,\u201d Eric says. \u201cIt\u2019ll often use one word by default, which often doesn\u2019t match the brand\u2019s creative intent.\u201d<\/p>\n

These persistent missteps often make machine translation not worth the hassle for major site-wide use. Further, machine translation alienates customers, says Blas Giffuni, director of MotionPoint\u2019s Global Growth team. The stakes can be much higher than simply presenting translated content that lacks verve, or is challenging to understand, he says.<\/p>\n

\n

Grievously mistranslated content becomes a serious liability and credibility problem for a brand.<\/p>\n<\/blockquote>\n

\u201cWe\u2019ve witnessed software-generated missteps when clients used machine translation,\u201d Blas says. For instance, an outdoor retailer\u2019s descriptions for \u201cshotguns for hunting turkeys\u201d was mistranslated by software into Spanish as \u201cshotguns for shooting Turkish people.\u201d Such grievously mistranslated content becomes a serious liability and credibility problem for a brand.<\/p>\n

Another liability: Google\u2019s search algorithm doesn\u2019t look favorably on machine-translated content. It doesn\u2019t follow the company\u2019s quality guidelines<\/a>.<\/p>\n

\u201cGoogle cares about user experience,\u201d Blas explains. \u201cMachine translation rarely delivers well-written content, which is the very thing Google is looking for. It can penalize these sites, often by lowering SERP<\/a>\u2014or in especially bad cases, removing the site from Google\u2019s index altogether.\u201d<\/p>\n

To Be Continued\u2026<\/h2>\n

The business reasons to leverage human translation in website translation projects transcend brand voice and search-engine visibility.<\/p>\n

We\u2019ll soon publish the conclusion to this two-part blog series. In that post, you\u2019ll discover how human-powered translations\u2014complemented by powerful translation-management technologies\u2014can increase the impact of websites in international markets, wildly improve global SEO, and reduce costs compared to other solutions.<\/p>\n<\/p>\n

Stay tuned! 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