In today’s interconnected world, legal disputes transcend borders and languages. The frequency of cross-border litigation will only continue to rise as global business interactions surge and regulatory complexities multiply. From data collection to review and production, a strategic approach is necessary to successfully manage foreign language discovery.

The following are several areas requiring mastery for the challenges of navigating multilingual discovery.

1. Understanding Data Collections

In multinational disputes, custodians are often scattered globally, resulting in data generated in multiple languages. Identifying their precise locations provides early insights into the potential foreign language sources and any data privacy implications. After gaining a comprehensive understanding of data collections, strategies can be implemented to streamline their review, optimizing efficiency, cost-effectiveness and timing.

2. Evaluating the Suitability of Machine Translation

Modern machine translation tools offer efficiency, but their suitability depends on the language and the content complexity. Combining machine translation and human language expertise is often required to effectively address challenging languages or complex content.

3. Running Language Identification

Language identification technology automates language-based sorting, accurately identifying the predominant language in a document and the percentage of content in each language, eliminating the need for English-speaking reviewers to manually sift through them. This reduces time and costs, improving resource planning and ensuring documents are routed to the appropriate workflows sooner rather than later.

4. Conducting Early Case Assessment

Beyond language identification, early evaluation of data provides vital insights for optimizing the review process. ECA allows a deeper view into the content and context of documents, enabling the identification of high-value documents suitable for native speaker reviewers, while lower-value content like junk files or email blasts may follow alternate routes.

5. Incorporating Traditional Analytics Tools

Technology-assisted review and threading are among the tools that can supercharge foreign language workflows. TAR utilizes machine learning algorithms to identify patterns and relevance criteria within documents, making it language-agnostic and highly effective in sifting through multilingual content. Email threading organizes email exchanges into conversations, highlighting the context and relationships within them. Review is accelerated and redundancy reduced, as the same content is only assessed once. These tools, along with machine translation software, greatly enhance efficiency and precision in foreign language reviews.

6. Enhancing Machine Translation Engines

Machine translation can expedite review, but fine-tuning its engine can improve quality. Basic capabilities are a good starting point, but working with linguistics experts to customize the solution to the specific case subject matter can optimize accuracy.

7. Centralizing Translations

Efficient organization, including version control, robust indexing and linking translations to their originals, is vital when dealing with multilingual discovery. Translation versions can be seamlessly uploaded to a central hosting database, facilitating retrieval based on keywords, dates or other relevant criteria. Centralizing simplifies the workflow, reduces errors and enhances accessibility.

Looking Ahead

The future of translation technology, driven by generative AI, holds promise for faster and more innovative solutions with great potential in the legal field. AI-powered voice translation technology can minimize delays, reduce costs and streamline communications in depositions. It also simplifies the transcription and translation of spoken content from audio and video records, enabling efficient processing of spoken evidence.

Despite challenges and inherent risks, AI models are advancing quickly in grasping context, nuances and cultural references, all of which promise to automate translations and reduce reliance on traditional machine translation.