The Evolving Landscape of Localization
When we mention localization and artificial intelligence (AI) in the same conversation, machine translation (MT) and machine learning (ML) often come to mind. However, AI’s role in localization goes beyond MT and ML. AI can be seamlessly integrated into all stages of a localization project, from translation to quality assurance.
In this article, we will explore how AI is transforming the localization industry and the key differences between traditional and AI-powered approaches.
The Impact of AI on the Localization Industry
Countless sectors are embracing the potential of AI, particularly ML, deep learning (DL), neural machine translation, and text-to-speech technology (TTS). Speech recognition, AI, speech synthesis, and natural language processing in the localization industry have witnessed significant advancements. However, some argue that no matter how advanced the algorithms become, they can never replace the human mind.
Nevertheless, AI offers exciting possibilities within the localization sector, including:
Localization Becomes More Authentic with Machine Learning
AI technology has the ability to monitor language evolution, enabling the identification of outdated translations. It can also handle idioms and sayings across multiple languages and adapt to regional language variations.
With AI’s capacity to extract and analyze vast amounts of real-time data, ML and DL can identify slang and incorporate the most authentic and up-to-date language into localized content.
AI Improves the Efficiency and Turnaround Time of Localization Projects
Traditional localization tools provide accurate translations but may not always match the speed requirements. AI helps expedite the localization process, allowing companies to complete more projects in less time and at a reduced cost.
Images and Audio Can Be Localized with AI
Localizing images and audio is essential but often time-consuming. The ability to localize these elements represents a significant breakthrough in the field of AI.
Artificial intelligence also powers machine translation software, which is now extensively used. Moreover, the localization industry has adopted new approaches such as augmented translation and adaptive MT to enhance the output quality of machine translation, resulting in faster localization processes.
Comparing Traditional vs. AI-Powered Localization Processes
Language Model
Traditional localization relies on human translators who require sufficient context to consistently translate accurately. They work with more contextual information to understand the product they are localizing. In contrast, AI facilitates context-independent translation, allowing translators to work with single strings of data without explicit context. AI-powered localization is capable of covering a wider range of expressions, including slang, local dialects, incomplete sentences, and irregular syntax.
Localization Process
According to experts, traditional localization involves sequential steps, such as translation, editing, and quality assurance. Translators often use tools like translation management systems (TMS) or computer-assisted translation (CAT) tools.
In traditional localization, translators must review and ensure complete translation of large volumes of content before passing it to another team member. However, AI-powered localization simplifies the process by enabling string-based translation. This allows multiple sources to translate strings of data from a single file without the need for an extensive understanding of the broader context.
Resources
Traditional localization heavily relies on linguists’ expertise in various domains, such as IT, legal, marketing, and medical industries. Linguists employ CAT tools, industry guidelines, and translation rules when localizing content.
Contrastingly, AI-powered localization requires linguists to have a deep understanding of language, local expressions, and cultural nuances, rather than extensive domain-specific knowledge.
The Future of AI and Localization
Businesses that rely on traditional localization methods strive for faster, more efficient, and cost-effective solutions. As a result, traditional approaches are gradually adopting a more agile nature. However, we are only witnessing the initial stages of machine-enhanced human intelligence.
Localization service providers that fail to embrace AI may fall behind in our digital age. Although AI will not replace human translators anytime soon, it is crucial for professionals to harness AI technologies and computational power to deliver accurate and efficient localization services that meet modern expectations.
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