It’s a story that’s as old as time. Every time a new technology emerges, you have two groups of people. One group is excited about the potential applications and the other panics about the change ahead and potential disruption. AI in the translation industry has sparked similar reactions.
Will AI take away translators’ jobs? Will language service providers be forced to compete with machines? What will the impact on the economy be?
As with any technology, the amount of good that AI will bring to the translation industry depends on how it’s used and how much we, as humans, invest in understanding its potential.
At Ciklopea, we were always curious about the innovative ways technology can help people work better and support clients in delivering localized experiences while saving time and money. Here’s where we stand when it comes to the controversy surrounding AI in translation.
Fear of Change Stands in the Way of Progress
When your only tool is a hammer, you see nails everywhere you go. The same goes for being overly set in your way of thinking. If you insist on looking for the dangers of AI for the translation industry, you’ll find them. But this short-sightedness prevents you from seeing the potential of AI.
The way you can beat this is by shifting your perspective and replacing fear of the unknown with curiosity. AI can help translators and language service providers as a whole in tremendous ways. Ask yourself the following questions:
- How can AI help language experts become more efficient in delivering translations or collaborating?
- Is there a way to leverage AI to ensure more consistency and accuracy in translations?
- Can AI help us break the language barrier faster?
- Can AI support human creativity in the localization process?
- What are the new and positive opportunities AI brings to the language market?
You probably don’t know how to answer all these questions immediately, but they serve as good prompts to challenge your old way of thinking. You can do independent research, of course, and you can also keep reading to discover what we see as the main benefits of AI in translation.
Machine Translation (MT) Can Boost Your Productivity by 60%
As technology advances, so do translation services. Machine Translation (MT) is the most obvious and most common use case for AI in translation. But it’s not just about inserting source text into a tool and then pasting it into a document and calling it a day. So don’t think that it’s just a fancy name for Google Translate.
Machine Translation is about improving efficiency and accelerating time-to-delivery. The combination of MT and advanced artificial intelligence systems allows translators to work better and faster.
An average translator can translate between 2000-2500 words per day without any help from MT. Imagine how much faster turnaround times could be if we let machines do the heavy lifting? According to our calculations, it’s somewhere between 50-60%.
This benefit for the translator creates a benefit for the end client, too. For projects that don’t require transcreation and heavy localization – i.e., detailed and careful checks of tone, style, and the cultural values of the target audience – MT brings a lot of benefits.
|Did you know? Ciklopea has earned the official ISO 18587 Certification for Human Post-Editing. For certain projects, the ideal approach has proven to be a combination of human expertise and machine assistance. This is how you get accurate and culturally appropriate translations. Translated by machines. Verified by humans.
Even in cases where the human touch is integral to the process, MT can help with the initial brainstorming or with kicking off the translations so that language pros don’t have to start from scratch.
CAT Tools Powered by AI
If you’re a translator, you’re probably using some form of AI in your everyday life without even being aware of it. It doesn’t have to be shiny or marketed with power words in order for it to be groundbreaking. Take CAT tools as an example.
AI-powered CAT tools help human translators by suggesting translations based on previous work. They can create translation memories and identify inconsistencies. Imagine if a translator has been working on a tedious project, translating hundreds of pages of legal documents, and within a tight deadline.
These tools minimize the chances for human error, they can boost productivity, and make sure you achieve language consistency. Just think about advanced grammar and spell checkers, source and target text search, concordance search, and more.
Standard CAT tools we’ve used at Ciklopea (or are actively using) include Across Language Server, SDL Trados Studio, SDL GroupShare, SDL Passolo, memoQ, Phrase, Wordfast, Translation Workspace and others, and they come both as desktop software and cloud-based solutions.
|Find out more: Learn how Ciklopea uses Orchestrum to increase productivity by up to 85% thanks to process automation.
Natural Language Processing (NLP) Helps with Sentiment Analysis and Translation Efficiency
NLP is a field of AI that focuses on the interaction between computers and human language. It relies on algorithms and models to enable machines to understand, interpret, and generate human-like language.
So, how is it different from Machine Translation (MT)?
The primary goal of MT is to produce accurate and coherent translations from a source language to a target language. On the other hand, NLP aims to enable computers to understand the meaning, context, and nuances of human language. This includes syntax, semantics, and pragmatics.
In a nutshell, they are both branches of AI that deal with language, but they serve different purposes and involve different technologies.
Here are some examples of how NLP can be used:
|Sentiment analysis, named entity recognition, and text summarization are examples of applications where NLP is used to analyze and understand textual data.
|NLP is employed in systems that turn spoken language into written text.
|Chatbots and virtual assistants
|NLP powers the conversational abilities of chatbots and virtual assistants, enabling them to understand and respond to user queries in multiple languages.
In terms of techniques, NLP might rely on tokenization, which is breaking down text into smaller units, such as words or phrases. This can help translators to do their job more efficiently and without wasting energy on source text preparation. Of course, there is also syntax and semantic analysis, which refers to understanding the grammatical structure and meaning of sentences.
AI Supercharges the Quality Assurance Process
QA checklists for linguists are long. They have to be, to ensure great-quality translations. QA experts need to evaluate and review consistency, verify terminology, check for grammar and syntax errors, make sure translations adhere to the style guide, and more.
Luckily, AI has the power to accelerate the QA process and make things a bit simpler. To be more precise, technology can help us with:
- Identifying inconsistencies (e.g., variations in phrasing, contradictory terminology)
- Contextual understanding (e.g., flagging idiomatic expressions that don’t match the ones stored in translation memory systems)
- Quality scoring (e.g., introducing scoring mechanisms and pointing out areas in MT-assisted translations where human attention is needed)
- Automated error reporting (e.g., categorizing and reporting different types of errors, such as spelling mistakes or grammar issues)
Large Language Models (LLMs) are based on AI-powered algorithms and are incredibly powerful in terms of generating, summarizing, and predicting text. These language models can be used not only for generating and classifying text but for direct translation of text as well.
LLMs are generally considered to be “the next step” in MT. They are particularly helpful for languages that are rich in homonyms, for recognizing context, and for properly interpreting the use of reflexive structures in certain languages. Because they have been trained on large datasets, they can help with the quality of MT output (i.e., grammatically correct and well-structured sentences that are contextually appropriate and accurate).
A more advanced version of AI-powered quality assurance implies using predictive analytics. AI algorithms can predict potential translation errors based on historical data and patterns. Just think of the possibilities! You can proactively identify and correct issues before they turn into bigger problems or even blockers on a project level.
Don’t Ignore AI’s Potential for Translation and Localization
AI is here to help you work better and save money, not to steal your job. Absolutely everyone in the translation ecosystem can benefit from it. If you have repetitive content you need translated, with MT you can get it done much faster. Just think about manuals, user guides, or privacy policies.
Here’s how everyone wins:
With MT, translators don’t have to start from scratch. They can post-edit the content and do quality assurance so everything is ready much faster than it would be if they weren’t using tech.
And what does this mean for you? Lower costs, faster translation turnaround, and better translation quality. With machine translation and AI, you can cut costs where human post-editing is sufficient, and then allocate the remaining resources to more complex projects that require creativity or transcreation.
Want to explore how Ciklopea can help you? We hold all the relevant ISO certifications and have more than 20 years of experience. There’s a reason why we’re one of the most trusted language service providers in Europe. Schedule a call today to discuss your project.