Cross-Lingual Knowledge Transfer Framework for Multilingual Customer Support AI: An Expert System Perspective

Abstract

This study presents a comprehensive methodology for processing multilingual customer support data to prepare it for training AI-based conversational systems. Using a dataset of 36,599 unique customer interactions from a Finnish energy company, we employed language-specific BERT models to identify and analyse thematic patterns within customer inquiries. Our dual-model approach separately processed Finnish and English content, utilizing specialized embeddings for each language before comparing topics across languages using cosine similarity measures. Our preprocessing pipeline employs confidence-weighted language detection optimized for technical domain terminology, adaptive frequency filtering with domain preservation and morphology-aware language processing to address the unique challenges of multilingual customer support data. Results revealed distinct topic distributions between languages with strategically important overlapping themes, particularly in technical domains where similarity coefficients reached 0.700. UMAP visualizations confirmed the computational findings, showing well-defined clusters in Finnish content and more diffuse boundaries in English topics. The identified cross-language similarities in contract and energy-related topics provide promising opportunities for targeted knowledge transfer, while disparities in other domains highlight the need for language-specific optimization. The methodology presented demonstrates a robust framework for analysing bilingual customer support corpora that can be extended to training specialized AI assistants while addressing language-specific nuances.