Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources

Understand

For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).

• While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples.
• To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities.
• To further improve the model's generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training.