How AI Impacts Tools, Translation, and Teams
Artificial intelligence (AI) is becoming an integral part of most industries—and translation is no exception. Today, at least half of language service providers (LSPs) use some form of machine translation (MT). And 93 percent of the most mature LSPs are MT capable, meaning they use it to some extent. Of course, it’s easy to see why. Machine translation can significantly reduce costs and turnaround times, allowing providers to serve their clients better.
MT isn’t the only tool in the translation toolbox, either. AI-powered technologies can automate workflows, streamline translations, and promote cross-functional collaboration. Yet, like any technology, AI can also disrupt industries and alter the role of humans in the workplace. As AI advances, staying up-to-date and adapting to its capabilities is essential. Doing so will make the difference between success and failure in any organization.
With all this in mind, let’s explore how AI impacts tools, translation, and teams across the language services industry.
How AI Supercharges Translation and Localization Tools
Believe it or not, researchers have been experimenting with machine translation since the 1950s. These early systems translated text from one language to another using linguistic rules and dictionaries. Fast forward to the 2010s, and innovations in deep learning and artificial neural networks have enabled the development of models that generate near-human-like text and speech. Now, they power some of the language industry’s most commonly used translation tools.
Here’s a look at how different branches of AI have been integrated into language translation, speech-to-text, and voice recognition applications.
Natural language processing (NLP)
Natural language processing focuses on enabling computers to understand, interpret, and generate human language. NLP models power:
- Neural machine translation (NMT) In recent years, AI-driven NMT has greatly improved automated language translation accuracy and fluency. LSPs frequently use it to translate documents, websites, and chatbots in real-time.
- Text analysis NLP extracts insights from large volumes of text. These include entity recognition, parts-of-speech tagging, and dependency parsing to understand text structure and meaning. Linguists can use NLP for entity recognition to identify proper nouns, such as names of people, places, or organizations. And it can accurately translate them while maintaining the context and structure.
- Language generation NLP models like GPT-3 generate human-like text. Translators can leverage these models to create personalized content such as marketing materials, emails, or product descriptions to meet the needs and preferences of the target audiences.
- Language understanding Chatbots and virtual assistants use NLP to understand and respond to natural language queries and commands. Linguists can train these NLP models in different languages to provide multilingual customer support.
Sentiment analysis, or opinion mining, involves determining the sentiment or emotion expressed in a text. AI models classify text as positive, negative, or neutral. Linguists can use this to assess whether a translation conveys the intended emotion or sentiment. For example, a translator may use sentiment analysis to ensure a translation of a customer service message accurately conveys the desired tone to the customer.
AI models, particularly extractive and abstractive summarization models, automatically produce concise summaries of lengthy documents or articles. A text summarization tool can help translators quickly grasp the main points of a long document. These tools are especially useful when dealing with voluminous source materials, allowing translators to focus on the most relevant information.
Speech recognition technology drives transcription services, voice assistants, and other voice-activated applications. It converts spoken language into text and enables users to interact with their devices using natural commands. Translators use it to generate transcripts for written translation. Plus, voice-enabled localization uses speech recognition to adapt software, content, or applications to specific languages.
How AI Streamlines Translation
Now that you know more about the technology behind the tools, let’s dive deeper into how they impact translation. Here are some ways AI automates and optimizes various aspects of the process.
Translation projects involve numerous files, and keeping track of them can be challenging. AI automates translation file management by:
- Converting file formats
- Extracting text from various sources
- Organizing files
- Batch processing
It integrates with translation memory tools, automates quality control, and streamlines workflows based on predefined rules. Improving efficiency, reducing errors, and accelerating the translation process.
Speed, efficiency, and scalability
Unlike human translators, machine translation can process large volumes of text in seconds, making it ideal for informative and non-creative content that doesn’t require a great deal of nuance. Think technical documentation or customer service information. It also frees up time for translators to focus on the creative aspects of projects. In e-commerce, for example, MT can quickly translate product descriptions while translators work on marketing or promotional copy.
Machine translation post-editing (MTPE)
While machine translation is highly accurate, some types of content still require a human touch. Once the project is complete, translators can refine and edit the content instead of starting from scratch. This approach accelerates the translation process, allowing for the rapid delivery of high-quality translations while reducing the workload and time required for each project.
Audiences today expect content that matches their interests and preferences. To meet this demand, translation teams rely on content personalization tools that analyze user data to personalize content for different audiences. For example, a streaming platform’s recommendation engine will feature the most popular TV shows and movies in the viewer’s preferred language or region.
Translations require consistent language usage and tone—especially regarding a company’s brand. However, this can be difficult to maintain across websites, marketing materials, and other content. Linguists use translation memories and terminology management tools to ensure that every translation meets these guidelines. Which helps to reduce the chances of errors or discrepancies when multiple translators work on the same project.
Quality assurance (QA)
Even highly skilled translators and advanced MT systems make errors from time to time. That’s why QA is an essential part of every translation workflow. AI-powered tools can automatically review translations for errors, inconsistencies, and potential issues with grammar or terminology.
How AI Transforms Translation and Localization Teams
Considering AI’s influence on translation workflows, you might wonder what this means for the teams involved. Despite fears that AI will replace humans, it opens up new opportunities to focus on creativity, strategy, and problem-solving.
Here’s how different teams are reimagining their roles in the translation industry.
Due to the adoption of machine translation, the role of human translators often encompasses new tasks such as editing, content creation, and content strategy. This can entail:
- Refining machine translation output or MTPE
- Creating localized content for different regions
- Analyzing content to develop new strategies
Human translation is increasingly reserved for projects that require creativity and an understanding of linguistic and cultural nuances. For example, advertisements, slogans, and campaigns frequently rely on creativity to be effective, and legal translations often require a deep understanding of legal systems and terminology in two languages.
Data analytics teams
As translation and localization become data-driven, data analytics teams play a more prominent role in the language services industry. They improve translation quality, optimize processes, and provide valuable insights into customer behavior. Here are some examples of how they do it:
- Analyzing AI-powered tools’ performance and fine-tuning them for specific languages and domains.
- Uncovering patterns, trends, and user preferences in localized content to assist decision-making.
- Creating models that optimize resource allocation based on historical data and market trends.
Combined with the expertise of linguists, subject matter experts, and data scientists, these models can help organizations better understand customer needs and optimize operations.
AI also has a significant impact on project managers. It greatly enhances their abilities to streamline various aspects of project management, saving LSPs time and money. Here are some examples of how AI-powered tools can do just that:
- Project management tools automate task assignments, project timelines, and resource allocation, making it easier to create efficient schedules.
- Cost prediction tools analyze historical project data to provide more accurate cost estimates, helping project managers set budgets.
- Real-time progress monitoring tools provide instant updates on project progress, flagging delays or issues. This allows project managers to intervene promptly to keep the project on track.
As AI takes over more aspects of project management, project managers will likely shift toward program management. This will enable them to focus on the strategic aspects of projects rather than on day-to-day tasks.
Disruptive AI is Here to Stay
Ultimately, the role of AI will continue to evolve. Tools will become increasingly sophisticated and efficient, allowing for greater accuracy and scalability. Machine translation will take on more tasks, and teams will take on roles that require a blend of creativity, problem-solving, and technical skills. All in all, AI will revolutionize the translation and localization industry, changing the way we work and the way we communicate.