Empirical Evaluation of Ensemble and Single Model Approaches for Multilingual Predictive Coding

Corporate litigation costs associated with manually reviewing documents in legal matters continue to rise, driven by the ever-expanding volumes of data generated by businesses. To address this challenge, legal teams have increasingly turned to text classification techniques, commonly referred to in the legal community as predictive modeling, predictive coding, or "Technology Assisted Review (TAR)", to identify relevant documents more efficiently during the discovery phase of legal matters. By streamlining the classification process, predictive coding has proven instrumental in minimizing both the time and cost associated with legal discovery. As more corporations expand their operations across global regions, legal matters increasingly involve multilingual datasets, introducing significant complexities for the application of predictive coding. Traditional predictive models exhibit bias towards the dominant language within the model’s training data. The prevalence of multiple languages within the same document further complicates the challenge. This can lead to inefficiencies, elevated costs, and risks of inaccurate classifications of nondominant language documents.This research empirically examines two methods for applying predictive coding to multilingual datasets: a single multilingual model approach and a language-specific modeling approach. This group of collaborators posits that language-specific models will outperform the single multilanguage model approach in evaluating document relevance. The research used a multilingual dataset from a recent confidential real-world legal matter containing predominantly English and Chinese document text Performance was assessed using precision and recall measures. Our results show that the ensemble approach – employing language-specific models for classifying documents in their respective languages and employing the mixed language model across all remaining documents—outperforms a single multilanguage model. The outcomes of this research aim to inform the workflow considerations for legal professionals undertaking multilingual discovery efforts. The findings will help legal practitioners make data-driven decisions on the most efficient and accurate modeling approach for multilingual document review, shaping the future of predictive coding in cross-linguistic e-discovery.