Five first-class features to look for in AI-powered translation tools
By Chris Jacob, Chief Product Officer at Language I/O
Machine translation is a technology that uses computer software to automatically translate text between languages without human intervention. Powered by advanced AI, modern machine translation tools have become increasingly sophisticated, capable of understanding context and generating highly accurate translations.
Since AI’s rapid rise to prominence, multilingual capabilities for businesses seem within reach. Companies must carefully select translation solutions that balance technological innovation with security, accuracy and regulatory compliance to truly capitalize on the potential customers they’re currently missing through language barriers.
Translation tool must-haves to drive CS success
AI-powered machine translation tools are especially valuable in fields like customer service, finance and travel, where precise communication is crucial. By enabling support agents to communicate effectively across language barriers, machine translation helps reduce miscommunication, shorten response times and improve overall service efficiency. As labor shortages grow and businesses seek more streamlined operations, these AI translation solutions have become an essential component of successful customer communication strategies. So, what key capabilities should you look for in a translation tool? We’ve done the research, and here are the top five features that can help bolster customer service success:
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Accuracy and context awareness. Large language models (LLMs) are general-purpose AI systems trained on vast amounts of data to process and generate natural-sounding language. While versatile, they provide translations that are necessarily the most accurate for specific contexts.
On the other hand, neural machine translation (NMT) represents a more advanced and specialized approach. It uses artificial neural networks to focus on specific industry contexts and terminology and trains on millions of sentence-specific translations. NMT continuously improves accuracy through iterative learning and will adapt to business-specific glossaries and terms.
The key difference is that while LLMs (like Google’s PaLM 2) offer broad language capabilities, NMT provides more precise, context-aware translations that are particularly valuable for customer service and business communications where accuracy and brand voice are crucial.
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Language coverage. The ideal translation solution should offer both breadth and quality of language coverage. Beyond simply offering many languages, the system must provide accurate, intelligible translations in each one, which is particularly challenging for rare languages.
Direct translation between language pairs is preferable to using English as an intermediary language, as multiple translations can decrease accuracy. The system should also account for how different languages handle industry-specific terminology — some may borrow English terms while others will use unique vocabulary.
When evaluating translation software, consider the specific languages your business needs and the quality and accuracy the system can provide in those languages. Further, investigate the direct translation capabilities between language pairs and keep in mind how it handles industry-specific terminology. Finally, make sure you have visibility into the provider’s roadmap for language expansion and improvement.
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Data security and privacy. Machine translation solutions, particularly in customer service, must implement robust security measures to protect sensitive data. Look for tools that have strong encryption for all data, especially personally identifiable information (PII). Automatic PII detection and masking before translation is a crucial aspect of this data security piece, as well as ensuring the tool has a zero data retention policy that deletes info immediately after translation. The tool shouldn’t store customer communications at all, even for model training.
The tool should also adhere to global security standards (ISO 27001, GDPR, SOC 2, HIPAA) and implement secure network protocols like TLS 1.2. These security protocols must extend to all third-party vendors and subprocessors as well.
Once implemented, maintain strict access control policies for employees and contractors. Continuous threat monitoring and comprehensive business continuity planning are also critical security practices — you can’t just set it and forget it. Regular updates and audits are necessary to ensure the highest level of security.
Given the increasing frequency of cyberattacks and data breaches, organizations must thoroughly evaluate potential translation vendors' security practices and ensure they maintain rigorous data protection standards throughout their operations.
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Multi-engine approach. Advanced translation systems can utilize multiple translation engines simultaneously, each with distinct strengths in specific areas. Some engines may perform better with certain language pairs, while others may excel in translating industry-specific content like legal or technical documents.
A multi-engine approach means the system is able to select the optimal engine for each specific translation task, improving translation quality through the comparison of multiple outputs and allowing for greater adaptability across different content types and language combinations. Further, multiple engines mean enhanced reliability — if one engine experiences issues, there will always be a backup available as a failsafe. This approach ensures consistent, high-quality translations by leveraging the strengths of each translation engine while also taking their individual limitations into account.
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Integrations and performance. To be most effective, translation platforms need robust integration capabilities, including seamless connection with existing business tools and CRM systems, support for multiple communication channels, API availability for custom applications and in-platform translation capabilities that eliminate the need to switch between apps and windows.
These platforms should be able to provide near-instantaneous text processing and real-time translation for live communication while also efficiently handling large volumes of content and quickly processing complex documents.
One final performance focus area should be scalability. The platform must adapt to business needs, handle varying content volumes and provide support for seasonal demand influxes while reducing dependency on hiring multilingual staff. A scalable solution can also rapidly deploy across different regions and be ready to go when needed. The combination of these features enables businesses to streamline their translation processes while still maintaining efficiency and quality across all customer communications.
For customer support applications, successful implementation depends on selecting solutions that combine comprehensive language coverage, strong security measures, smooth integration capabilities and rapid processing speeds.
By choosing the right translation technology, companies can effectively communicate across language barriers, better serve their global customers and expand their international presence.
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About the Author
Chris joined Language I/O in 2021 after experiencing firsthand the difficulties of supporting a global product with a largely monolingual support team. His time on the road presenting technical concepts using machine translation showed him the need for a translation service that understands the complexities of industry-specific jargon.
As Chief Product Officer, he has delivered translation solutions that make high-quality machine translations easily accessible to enterprise users. He aims to provide seamless translation experiences for corporate use cases while minimizing the effort required to onboard and maintain those services.
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