Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical Routing
<|reference_start|>Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical Routing: The Mixture of Experts (MoE) approach is well-suited for multilingual and code-switching (CS) tasks due to its multi-expert architecture. This work introduces the DLG-MoE, a Dynamic Language Group-based MoE optimized for bilingual and CS scenarios. DLG-MoE operates based on a hierarchical routing mechanism. First, the language router explicitly models the language and dispatches the representations to the corresponding language expert groups. Subsequently, the unsupervised router within each language group implicitly models attributes beyond language, and coordinates expert routing and collaboration. The model achieves state-of-the-art (SOTA) performance while also having unparalleled flexibility. It supports different top-k inference and streaming capabilities, and can also prune the model parameters to obtain a monolingual sub-model. The Code will be released.<|reference_end|>
@article{huang2024dynamic,
    title={Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech
  Recognition with Hierarchical Routing},
    author={Hukai Huang, Shenghui Lu, Yahui Shan, He Qu, Wenhao Guan, Qingyang
  Hong, Lin Li},
    journal={arXiv preprint arXiv:2407.18581},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18581},
    primaryClass={cs.CL cs.AI}
}