Lilt, the modern language service and technology provider, announced the launch of multilingual asset management, a new solution that enables companies to achieve higher quality localisation and deliver a more unified brand and voice globally through better, more automated linguistic data quality control.
Multilingual asset management enables companies to deliver a more unified global customer experience through higher-quality, more brand-aligned content. This new feature allows teams to react quickly to brand and terminology changes within their organisation, staying up-to-date with changing company preferences. The feature provides automated quality checks, identifying mismatches between a customer’s legacy TM and most current termbase. Once identified, a reviewer can assess each mismatch and update the TM to align with the current termbase.
“Linguistic data curation is an essential discipline for modern localisation, which depends on training custom machine learning systems. Translation quality is too often compromised by poorly maintained translation memories and glossaries that no longer conform to brand requirements,” said Lilt CEO Spence Green. “We want to help our customers deliver higher quality localisation across their global customer base, and this solution is an important step in that pursuit.”
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The first in a series of new quality innovations from Lilt, this feature provides centralised, tech-enabled enterprise linguistic asset control and maintenance. With this cloud-based tool built in the Lilt Platform, translators can now produce more accurate, brand-aligned translations and more easily collaborate on linguistic asset maintenance, eliminating the need for cumbersome offline work.
Multilingual asset management enhances Lilt’s existing linguistic asset management services, automating hours of manual work to enable better and faster linguistic data improvement. With this solution, companies can more easily maintain their TMs, improving them on a consistent basis rather than waiting months or years between cleanups, resulting in higher quality, more regularly maintained linguistic assets that increase translation quality. Finally, TMs that better reflect current customer preferences improve the quality of data that trains each customer’s unique instance of the Lilt Engine, providing a better machine translation system with more accurate suggestions.
The solution also provides automated quality checks for grammar, spelling, and more, helping customers build more accurate linguistic assets that enable better global content quality and consistency.
