Cross-lingual NER Performance with Multi-source Teacher-Student Learning in Low-Resource Languages

Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can alleviate the data-hungry problem for low-resource languages. However, few researches have focused on the scenario where the source-language labeled data is also limited in some specific domains. A common approach for this scenario is to generate more training data through translation or generation-based data augmentation method. Unfortunately, we find that simply combining source-language data and the corresponding translation cannot fully exploit the translated data and the improvements obtaine

Research goal: To what extent does multi-source teacher-student learning improve cross-lingual NER performance on low-resource languages compared to single-source approaches using unlabeled target data?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.