Label-Aware Multi-Level Contrastive Learning Robustness Under Varying Mixed-Language Training Data Proportions

Recently conversational agents effectively improve their understanding capabilities by neural networks. Such deep neural models, however, do not apply to most human languages due to the lack of annotated training data for various NLP tasks. In this paper, we propose a multi-level cross-lingual transfer model with language shared and specific knowledge to improve the spoken language understanding of low-resource languages. Our method explicitly separates the model into the language-shared part and language-specific part to transfer cross-lingual knowledge and improve the monolingual slot taggin

Research goal: What is the impact of varying the proportion of mixed-language contexts in the training data on the robustness of label-aware multi-level contrastive learning in cross-lingual spoken language understanding tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.