Software used to eat everything. But now it is software plus data, i.e. artificial intelligence, which is the biggest game changer. Half of this data is textual, which is, eventually, always multilingual. Every company should understand and communicate with its global customers, but only very few have the organization and technology to do so.
Language Operations is formed of cross-functional and multidisciplinary teams. Comparable with what DevOps did for software, LangOps enables AI applications by operationalizing the management of the badly needed textual data.
LangOps owns the company’s linguistic assets: high-quality multilingual content, domain knowledge graphs, and any enterprise specific terms. These are used to build and maintain LangOps most powerful tools:
- Language models for classification, text analytics, neural machine translation (NMT), and other language processing tasks;
- Multilingual knowledge graphs, which capture know-how in a curated and formalized system.
Nobody left behind
Textual applications such as chatbots, knowledge extraction, or even a seemingly simple product search are always built per language. That can quickly become cost prohibitive. Therefore, many companies will only understand their customers as long as they speak their language, mostly English.
LangOps delivers a platform which separates strings from their meaning. It deploys multilingual language models which can analyze, translate, and generate text in many languages. Know-how is formalized once, but deployed in many languages.
With LangOps, companies leave no customer behind, organizations leave no stakeholder behind, and eGovernment leaves no citizen behind. LangOps provides inclusivity and interoperability.
In a recent Forbes Technology article, council member Joao Graca stated that LangOps should be the new paradigm in globalization. Serving global markets is no longer about broadcasting translated content, but rather enabling companies and organizations to communicate with their stakeholders no matter what language they speak.
NMT is approaching human parity for many domains and language pairs thanks to algorithmic progress, computing power, and the availability of data. Quality Estimation and multilingual knowledge directs experts to passages which need human revision. LangOps has dramatic effects on translation costs, speed, and quality.
LangOps features software that automates translation and language management. Machine learning has revolutionized the process, but for many tasks the precision of rule-based approaches is required. It requires pragmatic engineering and a lot of experience to smartly assemble and configure the components like machines in a factory.
For example, LangOps elevates terminology to multilingual knowledge graphs. These are not only used for quality estimation and assurance, but also as the key meta data to drive processes. LangOps sunsets the CAT tool-based process, introduced by ESTeam’s CEO in the early 90s, and replaces it with an efficient multilingual data factory which makes optimal use of AI, NLP, and human know-how.
CLOUD-BASED SERVICE LEVELS
Humans in the Loop
The automation of the language factory is complemented by the part of the process which involves humans. Here, LangOps classifies linguistic assets, human resources, workflow rules, and projects in a unified system which is expandable, dynamic, and provides fallback paths.
LangOps enables 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.
From Cost to Revenue Driver
LangOps often originates in translation because that’s where multilingual data is created. While cutting globalization costs can be important, executives are more concerned about how LangOps can drive growth.
Machine translation allows enterprises to communicate instantly with their customers. Multilingual knowledge systems (MKS) enable e-commerce players to deploy language-neutral product search. On top of that, an MKS makes data repositories, systems, organizations, and even countries interoperable. LangOps can provide the unified semantics for the Internet of Things.
By owning the textual enterprise knowledge, LangOps boosts critical customer-facing activities such as customer support, chatbots, text analytics, spare part ordering, compliance, sales, and many more. Globally!