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We live in an era where the issue of digital transformation is increasingly recognized as a primary concern, and a key focus of executive management teams in global enterprises. The stakes are high for businesses that fail to embrace change. Since 2000, almost half (52%) of Fortune 500 companies have either gone bankrupt, been acquired, or ceased to exist as a result of digital disruption. It’s also estimated that 75% of today’s S&P 500 will be replaced by 2027, according to Innosight Research. Responding effectively to the realities of the digital world have now become a matter of survival as well a means to build long term competitive advantage.
When we consider what is needed to drive digital transformation in addition to structural integration, we see that large volumes of current, relevant, and accurate content that support the buyer and customer journey are critical to enhancing the digital experience both in B2C and B2B scenarios. Large volumes of relevant content are needed to enhance the customer experience in the modern digital era, where customers interact continuously with enterprises in a digital space, on a variety of digital platforms. To be digitally relevant in this environment requires that enterprises must increasingly be omni-market focused, and have large volumes of relevant content available in every language in every market they participate on a continuous basis.
This requires that the modern enterprise must create more content, translate more content and deliver more content on an ongoing basis to be digitally relevant and visible. Traditional approaches to translating enterprise content simply cannot scale and a new approach is needed. The possibility of addressing these translation challenges without automation is nil, but what is required is a much more active man-machine collaboration that we at SDL call machine-first human optimized. Thus, the need for a global enterprise to escalate the focus on machine translation (MT) is growing and has become much more urgent. However, the days of only using generic MT to solve any high volume content translation challenges are over, and the ability of the enterprise to utilize MT in a much more optimal and agile manner across a range of different use cases is needed to enable an effective omni-market strategy to be deployed. A one-size-fits-all MT strategy will not enable the modern enterprise to effectively deliver the critical content needed to their target global markets in an effective and optimal way. Superior MT deployment requires ongoing and continuous adaptation of the core MT technology to varied use cases, subject domain, and customer-relevant content needs. MT deployment also needs to happen with speed and agility to deliver business advantage, and few enterprises can afford the long learning and development timelines required by any do-it-yourself initiative.
Neural machine translation (NMT) has quickly established itself as the preferred model for most MT use cases today. Most experts now realize that MT performs best in industrial deployment scenarios when it is adapted and customized to the specific subject domain, terminology, and use case requirements. Generic MT is often not enough to meet key business objectives. However, the constraints to the successful development of adapted NMT models is difficult for the following reasons:
Given the buzz around NMT, many naïve practitioners jump into DIY (do-it-yourself) open-source options that are freely available, only to realize months or years later that they have nothing to show for their efforts. The many challenges of working with open-source NMT are covered here. While it is possible to succeed with open-source NMT, a sustained and ongoing research/production investment is required with very specialized human resources to have any meaningful chance of success.
The other option that enterprises employ to meet their NMT adaptation needs is to go to dedicated MT specialists and MT vendors, and there are significant costs associated with this approach as well. The ongoing updates and improvements usually come with direct costs associated with each individual effort. These challenges have limited the number of adapted and tuned NMT systems that can be deployed, and have also created resistance to deploying NMT systems more widely as generic system problems are identified.
As a technology provider who is focused on enterprise MT needs, SDL already provides existing adaptation capabilities, which range from:
This situation will now change and continue to evolve with the innovative new NMT adaptation solution being introduced by SDL.
The SDL NMT Trainer solution provides the following:
The initial release of the On Premise Trainer is the foundation of an ever-adapting machine translation solution that will grow in capability and continue to evolve with additional new features.
Research shows that NMT models are very dependent on high-quality training data and outcomes are highly dependent on the quality of the data used. The cleaner the data is, the better the adaptation will be, and thus after this initial product release, SDL plans to introduce an update later this year that leverages years of experience in translation memory management to include the appropriate automated cleaning steps required to make the data used as good as possible for neural MT model training.
The promise of the best AI solutions in the market is to continuously learn and improve with informed and structured human feedback, and the SDL technology is being architected to evolve and improve with this human feedback. While generic MT serves the needs of many internet users who need to get a rough gist of foreign language content, the global enterprise needs MT solutions that perform optimally on critical terminology, and are sensitive to linguistic requirements within the enterprise’s core subject domain. This is a solution that leverages a customer’s ability to produce high-quality adaptations with minimal effort in as short a time as possible and thus make increasing volumes of critical DX content multilingual.
If you'd like to learn more about what's new in Adaptable Neural Language Pairs, click here.