Language Specific Module

Language-specific modules (LSMs) are increasingly used in multilingual natural language processing (NLP) models to improve performance and address the challenges of handling diverse languages within a single system. Current research focuses on designing efficient LSM architectures, such as low-rank matrix approximations and modular pre-training, to mitigate the computational cost and parameter bloat associated with large-scale multilingual tasks. This approach aims to enhance both the accuracy and efficiency of multilingual models across various NLP applications, including machine translation, speech recognition, and question answering, by disentangling language-specific and language-agnostic information. The effectiveness of LSMs in reducing negative interference between languages and facilitating positive transfer learning is a key area of ongoing investigation.