Info Bottleneck Quantized Multilingual Embeddings for Low-Code Cross-Cultural Opinion Analysis

Abstract

We introduce InfoBottleneck Quantized Multilingual Embeddings, a novel framework for low-code cross-cultural opinion analysis that addresses the dual challenges of semantic alignment and quantization robustness in multilingual settings. The proposed method reformulates cross-language embeddings by decomposing them into language-invariant and language-specific features, trained jointly under an information bottleneck objective to minimize redundancy while preserving task-relevant information. A transformer-based encoder projects input tokens into a shared latent space, where masked attention suppresses language-specific biases and auxiliary adapters encode cultural nuances with per-language quantization groups. The framework integrates a dynamic gating mechanism to adapt embeddings to event-specific lexicons, enabling real-time cultural adaptation without retraining. Furthermore, quantization-aware training ensures resilience to precision loss, achieving a 4x reduction in memory footprint while maintaining over 90% accuracy on low-resource sentiment tasks. The disentangled design allows seamless integration with downstream modules, including few-shot sentiment classifiers and opinion shift detectors, which leverage the quantized embeddings for cross-lingual alignment and culture-dependent sentiment analysis. Experiments demonstrate significant improvements over conventional multilingual embeddings, particularly in scenarios involving low-bitwidth deployment and dynamic cultural contexts such as global events. The implementation, optimized with custom CUDA kernels, offers a practical solution for resource-constrained applications while advancing the state-of-the-art in multilingual opinion mining.