Winning with Language Operations (LangOps)

In a recent Forbes Technology article, council member Joao Graca states that Language Operations should be the new paradigm in globalization. He hits the nail on the head by saying that serving global markets is no longer about broadcasting translated content, but rather enabling businesses to communicate with stakeholders no matter what language they speak. LangOps is an enterprise function formed of cross-functional and multidisciplinary teams which efficiently operationalize the management of textual data. Neural machine translation (NMT) and multilingual knowledge management are indispensable tools to win, understand, and support global customers.

Release the Machine Translation Handbrake

NMT is approaching human parity for many domains and language pairs thanks to algorithmic progress, computing power, and the availability of data. Yet executives are still asking themselves why these breakthroughs have so far had only marginal effects on translation costs, lag, and quality.

The main reasons for this are a price model still based on translation memory (TM) match categories and the use of the timeworn formula IF Fuzzy < x% THEN MT. In addition, terminology – which is crucial for quality, process, and analytics – often leads a pitiful existence in Excel columns or sidelined term bases. While most focus on how to squeeze the last, rather meaningless drop of BLEU score out of the NMT black box, the real benefits will only be delivered by a LangOps strategy carried out by an automated workflow and reliable resource management.

Language Operations

LangOps is built on software that automates translation and language management. AI and Machine Learning have revolutionized the process, but for many tasks a rule-based approach is still superior. As always in engineering, it’s a question of piecing it smartly and pragmatically together. For example, while NMT is replacing segment-based translation memories, the cheapest and best method will always be the recycling of previously translated content. Terminology is baked into both NMT and TM, and thus is easily overlooked. LangOps, on the other hand, elevates terminology to multilingual knowledge. It is not only used for quality estimation and assurance, but also as the key meta data to drive processes. LangOps builds a multilingual data factory optimized for costs, time, and quality needs.

AI with Experts-in-the-Loop

LangOps will enable the building of scalable language factories…and will power a move towards cloud-based service levels.

The efficiency of LangOps needs to be complemented by the part of the process which involves humans. LangOps classifies linguistic assets, human resources, workflow rules, and projects in a unified system which is expandable, dynamic, and provides fallback paths. For example, the workflow knows who has carried out a similar project before, who has expertise in a particular domain, or how many hours an expert will typically need for a specific task. LangOps will enable the building of scalable language factories that leave the outdated price-per-word business model in the dust of transactional translations, and will power a move towards cloud-based service levels.

Cut Costs, then Drive the Top-Line

LangOps typically starts with translation because that’s where enterprises have created their linguistic assets. While cutting globalization costs is important, executives are more interested in how LangOps can drive growth.

Machine translation allows enterprises to communicate instantly with their customers. Terminology databases can be upgraded to multilingual knowledge systems (MKS), which allow companies to not only broadcast localized content to global customers, but also actually understand them when they talk back. An MKS not only enables e-Commerce players to deploy language-neutral product search, but is also a proven solution to make data repositories, systems, organizations, and even countries interoperable. It also crucially provides the unified semantics for the Internet of Things. All of these benefits boost LangOps, which owns the normalized enterprise knowledge and is the basis for many critical customer-facing activities such as customer support, chatbots, text analytics, spare part ordering, compliance, and sales.

Get in touch with us here to learn more about how LangOps can grow also your top-line.

Author

Jochen is co-founder and CEO of Coreon GmbH. He also heads ESTeam AB, a leading LangOps company. As founder of TRADOS he had established Computer-Assisted Translation as a product category, with Semiox he is reinventing multilingual again. He is an internationally known software executive, business angel, and serial entrepreneur.