The Problem With LangOps

This is a letter I wrote to the editor of Multilingual after reading several articles focused on LangOps in the December 2022 issue.  This discussion started on LinkedIn and Cameron invited the active contributors to formalize our comments and write a letter to the editor with alternate viewpoints.

TLDR: LangOps is a term that refers to the vague use of "A.I."  in/around localization or is nothing more than a way to describe the centralization of enterprise translation production processes.

I carefully read all of the following before writing my letter to ensure that I had not somehow missed the boat.  The basic question I am still left with after looking carefully through the LangOps material is "Where's the real substance of this concept/idea/word?"

•  Article by Renato: LangOps- The Vision & the Reality 
• Article by Arthur Weitzel:  LangOps: Pipe Dream, LSP´s Heaven or Just a New Hashtag?
• Article by Andrew Warner:  On the Origin of LangOps - The evolution of the localization roadmap
• Article by Miguel Cerna: LangOps and Localization Integration rather than substitution?
• Article by Riteba McCallum: The LangOps Paradigm: Perceptions of machine translation within the translation industry 
• and of course the LangOps principles.

Jump to 2' 50" to get to the relevant part

Here is a slightly ornamented version of the text of my letter to the editor which was published in the Multilingual February 2023 issue. I include a version with emphasis (mine) so that others may also comment on this, and perhaps correct my misperception. 

Special Thanks to Marjolein Groot Nibbelink for taking the trouble to convert the letter to a really well-read audio track that can be played back faster.

Dear Multilingual Editor (Cameron),

After reading the various articles on LangOps in the Multilingual December 2022 issue, I had hoped that I would get a better sense of what LangOps is, and why it matters. But I cannot say that this happened for me, and I am not sure if I (or any other reader) have any more clarity on what LangOps is, beyond it being a vendor buzzword, that remains fuzzy and amorphous because there is not enough supporting evidence to document it properly. While there was much discussion about why a new definition that went further than localization is needed, there was not much that defined LangOps in more concrete terms. I suspect the fuzziness and lack of clarity that I felt are true for many other readers as well.

One is left asking. “Where’s the beef?” on this thing they call LangOps.

I reviewed the articles in the magazine on the LangOps subject again before writing this letter, to better identify the defining elements, and to make sure I was fair and had not missed some obvious facts. My intention with my comments here is to hopefully provide a coherent critique of the subject matter, which started in discussion with comments made by several readers about LangOps on LinkedIn. 

From my reading, the articles in Multilingual were clearer on Why new definitions are needed, but less clear on the What [it is] or explaining the How.

It appears to me that the LangOps concept is another attempt by some stakeholders in the industry to raise the profile of the translation business, to make it more visible at the executive level, or to increase the perceived value of the translation production process by imbuing it with more complexity and mysterious undefined AI elements. However, in the absence of specifics, it becomes just another empty buzzword that creates more confusion than clarity for most of us, especially so for new buyers. 

It is difficult to see how any sponsor could take the descriptions provided in this issue of Multilingual to a senior executive to ask for funding, or even to explain what it is.

It is clear that as the translation of some product and marketing content became recognized as a valuable international business-driving activity, the need to scale, organize and systematize it became more urgent and led to what most call localization today. 

Thus, localization I think refers to the many processes, activities, and tools, used in making language translation processes more automated, structured, and systematic. Most often this work is related to relatively static content that is mandatory in international markets, but recently it has expanded to include more customer service and support content. It also sometimes includes cultural adaptations that are made in addition to the basic translation. 

TMS systems have been central to the localization worldview over the past decade, as these TMS systems facilitate the development and management of different workflows, monitor translation work, and ease project management of distributed translation-related tasks (TEP). It is also true that MT has been minimally used in hard-core localization settings as MT systems were not deemed to be accurate, flexible, and simple enough to configure to be used in this work.

By carefully reviewing the published Multilingual articles again, I gathered that the following elements that are being used to define what LangOps is:

• There are AI-driven capabilities applied to certain localization processes which are not defined,
• Centralization of all translation production activities across the enterprise,
• Introduction of “more” technology into existing localization workflows, but what this is specifically, is unclear,
• LangOps is said to be made up of cross-functional and inter-disciplinary teams, but who and why is not clear,
• Possibly adding other value-added language tasks (sentiment analysis, summarization, chatbots) in addition to the translation. [This at least is clear].

To my view, the only element here that is clear in the many descriptions [of LangOps] is that of the centralization of translation production. 

The other elements used to describe what it is are kind of fuzzy and hard to pin down. They can mean anything or could mean nothing since vagueness is not easily pinned down. LangOps is another term, that is possibly even worse than localization (which confuses many regular people and many new customers) because it creates a communication problem. 

How do you answer the question, “What do you do?” in an elevator, a cab, at a party, on an airplane, with family and friends? As you can see both Localization and LangOps present opaque, obfuscating images to the regular human mind.

Would it not be so much easier to just say “Language Translation to Drive International Business”? And then maybe add, “We use technology, tools, and people to do it at a large scale efficiently.”

I would like to suggest a different way to view the continuing evolution of business translation. It is my feeling that the LangOps movement is linking the growing number of MT use cases, which have more dynamic IT connectivity, and cross-organization collaboration implications, with a need for a new definition.

We have now reached that perfect storm moment where most B2C and B2B businesses recognize that they need a substantial digital presence, that it is important to provide large volumes of relevant content to serve and please their customers, and that they need to listen to customers in social media, understand trends faster, and communicate across the globe much faster. 

This means that successful businesses have to share, communicate, listen, and produce translations at a much larger scale than they have had to in the past. The core competency from traditional localization work is less likely to be useful with these new challenges. These new market requirements need a shift away from TM and TMS-managed work to a more MT-centric view of the world. The volume of translation increases from thousands of translated words, a month, to millions or even billions of words a month to drive successful international business outcomes in the modern era. 

As Generative AI improves and begins to be deployed in production customer settings, we will only see the translation volumes grow another 10X or 100X. Thus, deep MT competence increasingly becomes a core requirement to be in the enterprise translation business.

MT has been improving dramatically over the last five years in particular, and it is not ridiculous to say that it is getting close to human output in some special cases when systems are properly designed and deployed by competent experts. 

Competence means that experts can quickly adapt and modify MT systems to produce useful output in the 20-30 different use cases where an enterprise faces an avalanche of text and/or audiovisual content. The new use cases go beyond the traditional focus of localization in terms of content and process. We now need to translate much more dynamic content related to customer services and support, translate more active communications (chat, email, forums), share more structured and unstructured content, pay more attention to social media feedback, and are just more real-time and dynamic in general.

The successful modern global enterprise listens, understands, communicates, and actively shares content across the globe to improve customer experience. Thus, I think it is fair to say that we (the translation business) are moving to a more MT-centric world from a previously TMS-centric world, and a critical skill needed today is deep competence with MT. 

Useful MT output means it helps grow and drive international business, even though it may not be linguistically “perfect”. The requirement for MT competence requires moving far beyond choosing an MT system with the best BLEU or COMET score. 

MT Competence means you can find egregious errors (MT & AI make these errors all the time) and instantly correct these problems to minimize damage. 

MT Competence means the skill and agility to respond to changing business needs and new content types and the ability to rapidly modify MT systems as needed. 

Competence in managing rapid, responsive, deep adaptation of MT systems will be a key requirement to actively participate as an enterprise partner (not vendor) on a global stage very shortly. 

When language translation is mission-critical and pervasive, the service provider will likely evolve from being a vendor to being a partner. It can also often mean that the scope of localization teams is greatly expanded and become more mission-critical.

While I can see a business reality where there is Machine-First & Human Optimized translation approach to content across the global enterprise, which requires responsive, continuously improving MT, it also means moving beyond traditional MTPE where clean-up crews come to reluctantly fix badly formed MT output produced by inexperienced and incompetent MT practitioners. 

However, the lights start to dim for me when I think of "LangOps" being part of this reality in any form whatsoever.

This continuing evolution of business translation also probably means that there is a much more limited role for the TMS or using it only for some localization (software and key documentation) workflows. The more common case as translation volumes grows is to connect all (Customer Experience) CX-related text directly into highly tuned, carefully adapted NMT systems in high-performance low-latency IT infrastructure that is directly customer-facing, or customer accessible. 

Recent data I have seen on MT use across a broad swathe of enterprise users shows that as much as 95% of MT use completely bypasses the TMS. Properly tuned expert-built MT engines do not need the unnecessary overhead of a TMS system. The enterprise objective is to enable translation at scale for everything that might require instant, and mostly but not necessarily a perfectly accurate translation, as long as it furthers and enhances any and every global business initiative and communication. 

Speed and scale are more important and have a more positive impact on international business success in many CX-related use cases than perfect linguistic quality does. The enterprise executives understand this even though we as an industry might not.

I am not aware of a single LangOps configuration or group on this earth or know any enterprise that claims to have such an initiative, but I can point to several massive-scale MT-driven translation engines around the world e.g. Airbnb, Amazon, Alibaba, and eBay where billions of words are translated regularly to drive international business and customer delight and serve a growing international customer base. I am confident we will see this pool of enterprise users grow beyond the eCommerce markets.

Thus, I see little value in promoting the concept of LangOps as what actually seems to be happening is that more expert-tuned enterprise MT is being used and we see the share of MT used to total translation volumes continue to grow. 

As this kind of responsive, highly adaptive MT capability becomes more pervasive across an enterprise, it also becomes a critical requirement for international business success. The activities related to organizing and managing significantly more dynamic content and translation volumes should not be mistaken to be something as vague as LangOps, as no organization I am aware of has the building blocks or template to create such a vaguely defined function. I think that it is more likely that Localization teams will evolve and the scope of their activities will increase, perhaps as dramatically as we have seen at Airbnb.

Airbnb just booked its first annual profit in its near-15-year history, a whopping $1.9bn in 2022. It now appears to be in rarefied air, with its place as the de facto online marketplace for homestays and experiences, giving it a network effect that’s hard to compete with.

 

I did find all the articles on LangOps useful in furthering my understanding, especially the ones by Riteba McCallum, and Miguel Cerna, and my comments should not be mistaken as a wholesale dismissal of the viewpoints presented. On the contrary, I think we have much more agreement on many of the core issues discussed. Though I do admit that I find the general concept of LangOps as it has been painted, to be a likely hindrance to our mutual future rather than a beneficial concept to drive our success with globalization and international business initiatives with our common customers.

Respectfully Yours,

Kirti Vashee

Here is the LinkedIn article where the discussion began:

P.S.  Maybe all I am saying is that LangOps just needs more cowbell 😄😄😄 to get the sound and the concept right?